{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.025957,
     "end_time": "2020-04-17T06:07:06.818332",
     "exception": false,
     "start_time": "2020-04-17T06:07:06.792375",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# COVID-19 Comparative Analysis\n",
    "> A Comparison of COVID-19 wtih SARS, MERS, EBOLA and H1N1\n",
    "- author: Devakumar kp\n",
    "- comments: true\n",
    "- permalink: /comparitive-analysis/\n",
    "- toc: true\n",
    "- image: images/copied_from_nb/covid-compare-1-1.png\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.020061,
     "end_time": "2020-04-17T06:07:06.859058",
     "exception": false,
     "start_time": "2020-04-17T06:07:06.838997",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "These visualizations were made by [Devakumar kp](https://twitter.com/imdevskp), from [this kaggle kernel](https://www.kaggle.com/imdevskp/covid-19-analysis-viz-prediction-comparisons)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-04-17T06:07:06.907111Z",
     "iopub.status.busy": "2020-04-17T06:07:06.906698Z",
     "iopub.status.idle": "2020-04-17T06:07:06.909280Z",
     "shell.execute_reply": "2020-04-17T06:07:06.908611Z"
    },
    "papermill": {
     "duration": 0.029403,
     "end_time": "2020-04-17T06:07:06.909395",
     "exception": false,
     "start_time": "2020-04-17T06:07:06.879992",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#hide\n",
    "# ! pip install folium plotly plotnine psutil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-04-17T06:07:06.952732Z",
     "iopub.status.busy": "2020-04-17T06:07:06.952177Z",
     "iopub.status.idle": "2020-04-17T06:07:06.954688Z",
     "shell.execute_reply": "2020-04-17T06:07:06.954229Z"
    },
    "papermill": {
     "duration": 0.026185,
     "end_time": "2020-04-17T06:07:06.954779",
     "exception": false,
     "start_time": "2020-04-17T06:07:06.928594",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#hide\n",
    "# ! npm install -g electron@6.1.4 orca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
     "iopub.execute_input": "2020-04-17T06:07:07.001769Z",
     "iopub.status.busy": "2020-04-17T06:07:07.001146Z",
     "iopub.status.idle": "2020-04-17T06:07:14.414990Z",
     "shell.execute_reply": "2020-04-17T06:07:14.414126Z"
    },
    "papermill": {
     "duration": 7.439942,
     "end_time": "2020-04-17T06:07:14.415110",
     "exception": false,
     "start_time": "2020-04-17T06:07:06.975168",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#hide\n",
    "# storing and anaysis\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import seaborn as sns\n",
    "from plotnine import *\n",
    "import plotly.express as px\n",
    "import folium\n",
    "\n",
    "from IPython.display import Javascript\n",
    "from IPython.core.display import display, HTML\n",
    "\n",
    "# color pallette\n",
    "cdr = ['#393e46', '#ff2e63', '#30e3ca'] # grey - red - blue\n",
    "idr = ['#f8b400', '#ff2e63', '#30e3ca'] # yellow - red - blue\n",
    "\n",
    "s = '#f0134d'\n",
    "h = '#12cad6'\n",
    "e = '#4a47a3'\n",
    "m = '#42e6a4'\n",
    "c = '#333333'\n",
    "\n",
    "shemc = [s, h, e, m, c]\n",
    "sec = [s, e, c]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.021765,
     "end_time": "2020-04-17T06:07:14.459066",
     "exception": false,
     "start_time": "2020-04-17T06:07:14.437301",
     "status": "completed"
    },
    "tags": []
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
    "execution": {
     "iopub.execute_input": "2020-04-17T06:07:14.515284Z",
     "iopub.status.busy": "2020-04-17T06:07:14.514681Z",
     "iopub.status.idle": "2020-04-17T06:07:15.014569Z",
     "shell.execute_reply": "2020-04-17T06:07:15.014078Z"
    },
    "papermill": {
     "duration": 0.534294,
     "end_time": "2020-04-17T06:07:15.014684",
     "exception": false,
     "start_time": "2020-04-17T06:07:14.480390",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/hostedtoolcache/Python/3.6.10/x64/lib/python3.6/site-packages/ipykernel_launcher.py:19: FutureWarning:\n",
      "\n",
      "Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "\n",
      "/opt/hostedtoolcache/Python/3.6.10/x64/lib/python3.6/site-packages/ipykernel_launcher.py:26: FutureWarning:\n",
      "\n",
      "Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "\n",
      "/opt/hostedtoolcache/Python/3.6.10/x64/lib/python3.6/site-packages/ipykernel_launcher.py:32: FutureWarning:\n",
      "\n",
      "Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Cases</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>nth_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Albania</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Angola</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date      Country  Cases  Deaths  nth_day\n",
       "0 2020-01-22  Afghanistan      0       0        0\n",
       "1 2020-01-22      Albania      0       0        0\n",
       "2 2020-01-22      Algeria      0       0        0\n",
       "3 2020-01-22      Andorra      0       0        0\n",
       "4 2020-01-22       Angola      0       0        0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide\n",
    "# COVID-19\n",
    "# --------\n",
    "\n",
    "# covid_19 dataset\n",
    "covid_19 = pd.read_csv('https://raw.githubusercontent.com/imdevskp/covid_19_jhu_data_web_scrap_and_cleaning/master/new/complete_data_new_format.csv', \n",
    "                       parse_dates=['Date'])\n",
    "\n",
    "# selecting important columns only\n",
    "covid_19 = covid_19[['Date', 'Country/Region', 'Confirmed', 'Deaths']]\n",
    "\n",
    "# replacing Mainland china with just China\n",
    "covid_19['Country/Region'] = covid_19['Country/Region'].replace('Mainland China', 'China')\n",
    "\n",
    "# renaming columns\n",
    "covid_19.columns = ['Date', 'Country', 'Cases', 'Deaths']\n",
    "\n",
    "# group by date and country\n",
    "covid_19 = covid_19.groupby(['Date', 'Country'])['Cases', 'Deaths']\n",
    "covid_19 = covid_19.sum().reset_index()\n",
    "\n",
    "# latest\n",
    "c_lat = covid_19[covid_19['Date'] == max(covid_19['Date'])].reset_index()\n",
    "\n",
    "# latest grouped by country\n",
    "c_lat_grp = c_lat.groupby('Country')['Cases', 'Deaths'].sum().reset_index()\n",
    "\n",
    "# nth day\n",
    "covid_19['nth_day'] = (covid_19['Date'] - min(covid_19['Date'])).dt.days\n",
    "\n",
    "# day by day\n",
    "c_dbd = covid_19.groupby('Date')['Cases', 'Deaths'].sum().reset_index()\n",
    "\n",
    "# nth day\n",
    "c_dbd['nth_day'] = covid_19.groupby('Date')['nth_day'].max().values\n",
    "\n",
    "# no. of countries\n",
    "temp = covid_19[covid_19['Cases']>0]\n",
    "c_dbd['n_countries'] = temp.groupby('Date')['Country'].apply(len).values\n",
    "\n",
    "c_dbd['new_cases'] = c_dbd['Cases'].diff()\n",
    "c_dbd['new_deaths'] = c_dbd['Deaths'].diff()\n",
    "c_dbd['epidemic'] = 'COVID-19'\n",
    "\n",
    "covid_19.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-04-17T06:07:15.110021Z",
     "iopub.status.busy": "2020-04-17T06:07:15.109318Z",
     "iopub.status.idle": "2020-04-17T06:07:15.291772Z",
     "shell.execute_reply": "2020-04-17T06:07:15.292278Z"
    },
    "papermill": {
     "duration": 0.241432,
     "end_time": "2020-04-17T06:07:15.292404",
     "exception": false,
     "start_time": "2020-04-17T06:07:15.050972",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/hostedtoolcache/Python/3.6.10/x64/lib/python3.6/site-packages/ipykernel_launcher.py:20: FutureWarning:\n",
      "\n",
      "Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "\n",
      "/opt/hostedtoolcache/Python/3.6.10/x64/lib/python3.6/site-packages/ipykernel_launcher.py:35: FutureWarning:\n",
      "\n",
      "Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "\n",
      "/opt/hostedtoolcache/Python/3.6.10/x64/lib/python3.6/site-packages/ipykernel_launcher.py:41: FutureWarning:\n",
      "\n",
      "Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Cases</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>nth_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-08-29</td>\n",
       "      <td>Guinea</td>\n",
       "      <td>648</td>\n",
       "      <td>430</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-08-29</td>\n",
       "      <td>Liberia</td>\n",
       "      <td>1378</td>\n",
       "      <td>694</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-08-29</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-08-29</td>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>1026</td>\n",
       "      <td>422</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-09-05</td>\n",
       "      <td>Guinea</td>\n",
       "      <td>812</td>\n",
       "      <td>517</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date       Country  Cases  Deaths  nth_day\n",
       "0 2014-08-29        Guinea    648     430        0\n",
       "1 2014-08-29       Liberia   1378     694        0\n",
       "2 2014-08-29       Nigeria     19       7        0\n",
       "3 2014-08-29  Sierra Leone   1026     422        0\n",
       "4 2014-09-05        Guinea    812     517        7"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide\n",
    "# EBOLA\n",
    "# ------\n",
    "\n",
    "# ebola dataset\n",
    "ebola_14 = pd.read_csv(\"https://raw.githubusercontent.com/imdevskp/ebola_outbreak_dataset/master/ebola_2014_2016_clean.csv\", \n",
    "                       parse_dates=['Date'])\n",
    "\n",
    "# ebola_14 = ebola_14[ebola_14['Date']!=max(ebola_14['Date'])]\n",
    "\n",
    "# selecting important columns only\n",
    "ebola_14 = ebola_14[['Date', 'Country', 'No. of confirmed, probable and suspected cases',\n",
    "                     'No. of confirmed, probable and suspected deaths']]\n",
    "\n",
    "# renaming columns\n",
    "ebola_14.columns = ['Date', 'Country', 'Cases', 'Deaths']\n",
    "ebola_14.head()\n",
    "\n",
    "# group by date and country\n",
    "ebola_14 = ebola_14.groupby(['Date', 'Country'])['Cases', 'Deaths']\n",
    "ebola_14 = ebola_14.sum().reset_index()\n",
    "\n",
    "# filling missing values\n",
    "ebola_14['Cases'] = ebola_14['Cases'].fillna(0)\n",
    "ebola_14['Deaths'] = ebola_14['Deaths'].fillna(0)\n",
    "\n",
    "# converting datatypes\n",
    "ebola_14['Cases'] = ebola_14['Cases'].astype('int')\n",
    "ebola_14['Deaths'] = ebola_14['Deaths'].astype('int')\n",
    "\n",
    "# latest\n",
    "e_lat = ebola_14[ebola_14['Date'] == max(ebola_14['Date'])].reset_index()\n",
    "\n",
    "# latest grouped by country\n",
    "e_lat_grp = e_lat.groupby('Country')['Cases', 'Deaths'].sum().reset_index()\n",
    "\n",
    "# nth day\n",
    "ebola_14['nth_day'] = (ebola_14['Date'] - min(ebola_14['Date'])).dt.days\n",
    "\n",
    "# day by day\n",
    "e_dbd = ebola_14.groupby('Date')['Cases', 'Deaths'].sum().reset_index()\n",
    "\n",
    "# nth day\n",
    "e_dbd['nth_day'] = ebola_14.groupby('Date')['nth_day'].max().values\n",
    "\n",
    "# no. of countries\n",
    "temp = ebola_14[ebola_14['Cases']>0]\n",
    "e_dbd['n_countries'] = temp.groupby('Date')['Country'].apply(len).values\n",
    "\n",
    "e_dbd['new_cases'] = e_dbd['Cases'].diff()\n",
    "e_dbd['new_deaths'] = e_dbd['Deaths'].diff()\n",
    "e_dbd['epidemic'] = 'EBOLA'\n",
    "\n",
    "ebola_14.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-04-17T06:07:15.514590Z",
     "iopub.status.busy": "2020-04-17T06:07:15.511576Z",
     "iopub.status.idle": "2020-04-17T06:07:15.694443Z",
     "shell.execute_reply": "2020-04-17T06:07:15.693480Z"
    },
    "papermill": {
     "duration": 0.378293,
     "end_time": "2020-04-17T06:07:15.694599",
     "exception": false,
     "start_time": "2020-04-17T06:07:15.316306",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/hostedtoolcache/Python/3.6.10/x64/lib/python3.6/site-packages/ipykernel_launcher.py:17: FutureWarning:\n",
      "\n",
      "Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "\n",
      "/opt/hostedtoolcache/Python/3.6.10/x64/lib/python3.6/site-packages/ipykernel_launcher.py:24: FutureWarning:\n",
      "\n",
      "Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "\n",
      "/opt/hostedtoolcache/Python/3.6.10/x64/lib/python3.6/site-packages/ipykernel_launcher.py:30: FutureWarning:\n",
      "\n",
      "Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Cases</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>nth_day</th>\n",
       "      <th>n_countries</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>epidemic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2003-03-17</td>\n",
       "      <td>167</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SARS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003-03-18</td>\n",
       "      <td>219</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SARS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2003-03-19</td>\n",
       "      <td>264</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>45.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>SARS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003-03-20</td>\n",
       "      <td>306</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>SARS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2003-03-21</td>\n",
       "      <td>350</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SARS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Cases  Deaths  Recovered  nth_day  n_countries  new_cases  \\\n",
       "0 2003-03-17    167       4          0        0            7        NaN   \n",
       "1 2003-03-18    219       4          0        1            9       52.0   \n",
       "2 2003-03-19    264       9          0        2           11       45.0   \n",
       "3 2003-03-20    306      10          0        3           12       42.0   \n",
       "4 2003-03-21    350      10          0        4           14       44.0   \n",
       "\n",
       "   new_deaths epidemic  \n",
       "0         NaN     SARS  \n",
       "1         0.0     SARS  \n",
       "2         5.0     SARS  \n",
       "3         1.0     SARS  \n",
       "4         0.0     SARS  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide\n",
    "# SARS\n",
    "# ----\n",
    "\n",
    "# sars dataset\n",
    "sars_03 = pd.read_csv(\"https://raw.githubusercontent.com/imdevskp/sars-2003-outbreak-data-with-web-scrapping-munging-and-cleaning-code/master/sars_2003_complete_dataset_clean.csv\", \n",
    "                       parse_dates=['Date'])\n",
    "\n",
    "# selecting important columns only\n",
    "sars_03 = sars_03[['Date', 'Country', 'Cumulative number of case(s)', \n",
    "                   'Number of deaths', 'Number recovered']]\n",
    "\n",
    "# renaming columns\n",
    "sars_03.columns = ['Date', 'Country', 'Cases', 'Deaths', 'Recovered']\n",
    "\n",
    "# group by date and country\n",
    "sars_03 = sars_03.groupby(['Date', 'Country'])['Cases', 'Deaths', 'Recovered']\n",
    "sars_03 = sars_03.sum().reset_index()\n",
    "\n",
    "# latest\n",
    "s_lat = sars_03[sars_03['Date'] == max(sars_03['Date'])].reset_index()\n",
    "\n",
    "# latest grouped by country\n",
    "s_lat_grp = s_lat.groupby('Country')['Cases', 'Deaths', 'Recovered'].sum().reset_index()\n",
    "\n",
    "# nth day\n",
    "sars_03['nth_day'] = (sars_03['Date'] - min(sars_03['Date'])).dt.days\n",
    "\n",
    "# day by day\n",
    "s_dbd = sars_03.groupby('Date')['Cases', 'Deaths', 'Recovered'].sum().reset_index()\n",
    "\n",
    "# nth day\n",
    "s_dbd['nth_day'] = sars_03.groupby('Date')['nth_day'].max().values\n",
    "\n",
    "# no. of countries\n",
    "temp = sars_03[sars_03['Cases']>0]\n",
    "s_dbd['n_countries'] = temp.groupby('Date')['Country'].apply(len).values\n",
    "\n",
    "\n",
    "s_dbd['new_cases'] = s_dbd['Cases'].diff()\n",
    "s_dbd['new_deaths'] = s_dbd['Deaths'].diff()\n",
    "s_dbd['epidemic'] = 'SARS'\n",
    "\n",
    "s_dbd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-04-17T06:07:15.748749Z",
     "iopub.status.busy": "2020-04-17T06:07:15.747990Z",
     "iopub.status.idle": "2020-04-17T06:07:15.952742Z",
     "shell.execute_reply": "2020-04-17T06:07:15.952282Z"
    },
    "papermill": {
     "duration": 0.234918,
     "end_time": "2020-04-17T06:07:15.952850",
     "exception": false,
     "start_time": "2020-04-17T06:07:15.717932",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Week</th>\n",
       "      <th>Region</th>\n",
       "      <th>New Cases</th>\n",
       "      <th>Year-Week</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>Other Countries</td>\n",
       "      <td>1</td>\n",
       "      <td>2012 - 12</td>\n",
       "      <td>2012-03-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>Republic of Korea</td>\n",
       "      <td>0</td>\n",
       "      <td>2012 - 12</td>\n",
       "      <td>2012-03-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>0</td>\n",
       "      <td>2012 - 12</td>\n",
       "      <td>2012-03-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>13</td>\n",
       "      <td>Other Countries</td>\n",
       "      <td>0</td>\n",
       "      <td>2012 - 13</td>\n",
       "      <td>2012-03-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012</td>\n",
       "      <td>13</td>\n",
       "      <td>Republic of Korea</td>\n",
       "      <td>0</td>\n",
       "      <td>2012 - 13</td>\n",
       "      <td>2012-03-26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Week             Region  New Cases  Year-Week       Date\n",
       "0  2012    12    Other Countries          1  2012 - 12 2012-03-19\n",
       "1  2012    12  Republic of Korea          0  2012 - 12 2012-03-19\n",
       "2  2012    12       Saudi Arabia          0  2012 - 12 2012-03-19\n",
       "3  2012    13    Other Countries          0  2012 - 13 2012-03-26\n",
       "4  2012    13  Republic of Korea          0  2012 - 13 2012-03-26"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide\n",
    "# MERS\n",
    "mers_cntry = pd.read_csv(\"https://raw.githubusercontent.com/imdevskp/mers_dataset_collection_cleaning/master/country_count_latest.csv\")\n",
    "mers_weekly = pd.read_csv(\"https://raw.githubusercontent.com/imdevskp/mers_dataset_collection_cleaning/master/weekly_clean.csv\")\n",
    "\n",
    "# cleaning\n",
    "mers_weekly['Year-Week'] = mers_weekly['Year'].astype(str) + ' - ' + mers_weekly['Week'].astype(str)\n",
    "mers_weekly['Date'] = pd.to_datetime(mers_weekly['Week'].astype(str) + \n",
    "                                     mers_weekly['Year'].astype(str).add('-1'),format='%V%G-%u')\n",
    "\n",
    "mers_weekly.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-04-17T06:07:16.010002Z",
     "iopub.status.busy": "2020-04-17T06:07:16.009377Z",
     "iopub.status.idle": "2020-04-17T06:07:16.012543Z",
     "shell.execute_reply": "2020-04-17T06:07:16.012135Z"
    },
    "papermill": {
     "duration": 0.034491,
     "end_time": "2020-04-17T06:07:16.012630",
     "exception": false,
     "start_time": "2020-04-17T06:07:15.978139",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bahrain</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>China</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country  Confirmed\n",
       "0  Algeria          2\n",
       "1  Austria          2\n",
       "2  Bahrain          1\n",
       "3    China          1\n",
       "4    Egypt          1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide\n",
    "mers_cntry.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-04-17T06:07:16.065461Z",
     "iopub.status.busy": "2020-04-17T06:07:16.064850Z",
     "iopub.status.idle": "2020-04-17T06:07:16.074697Z",
     "shell.execute_reply": "2020-04-17T06:07:16.074201Z"
    },
    "papermill": {
     "duration": 0.037792,
     "end_time": "2020-04-17T06:07:16.074809",
     "exception": false,
     "start_time": "2020-04-17T06:07:16.037017",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Week</th>\n",
       "      <th>Region</th>\n",
       "      <th>New Cases</th>\n",
       "      <th>Year-Week</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>Other Countries</td>\n",
       "      <td>1</td>\n",
       "      <td>2012 - 12</td>\n",
       "      <td>2012-03-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>Republic of Korea</td>\n",
       "      <td>0</td>\n",
       "      <td>2012 - 12</td>\n",
       "      <td>2012-03-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>0</td>\n",
       "      <td>2012 - 12</td>\n",
       "      <td>2012-03-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>13</td>\n",
       "      <td>Other Countries</td>\n",
       "      <td>0</td>\n",
       "      <td>2012 - 13</td>\n",
       "      <td>2012-03-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012</td>\n",
       "      <td>13</td>\n",
       "      <td>Republic of Korea</td>\n",
       "      <td>0</td>\n",
       "      <td>2012 - 13</td>\n",
       "      <td>2012-03-26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Week             Region  New Cases  Year-Week       Date\n",
       "0  2012    12    Other Countries          1  2012 - 12 2012-03-19\n",
       "1  2012    12  Republic of Korea          0  2012 - 12 2012-03-19\n",
       "2  2012    12       Saudi Arabia          0  2012 - 12 2012-03-19\n",
       "3  2012    13    Other Countries          0  2012 - 13 2012-03-26\n",
       "4  2012    13  Republic of Korea          0  2012 - 13 2012-03-26"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide\n",
    "mers_weekly.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.025218,
     "end_time": "2020-04-17T06:07:16.125892",
     "exception": false,
     "start_time": "2020-04-17T06:07:16.100674",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Reported Countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-04-17T06:07:16.185088Z",
     "iopub.status.busy": "2020-04-17T06:07:16.184498Z",
     "iopub.status.idle": "2020-04-17T06:07:36.340832Z",
     "shell.execute_reply": "2020-04-17T06:07:36.341313Z"
    },
    "papermill": {
     "duration": 20.189869,
     "end_time": "2020-04-17T06:07:36.341458",
     "exception": false,
     "start_time": "2020-04-17T06:07:16.151589",
     "status": "completed"
    },
    "scrolled": false,
    "tags": []
   },
   "outputs": [],
   "source": [
    "#hide\n",
    "fig = px.choropleth(c_lat_grp, locations=\"Country\", locationmode='country names',\n",
    "                    color=\"Cases\", hover_name=\"Country\", \n",
    "                    color_continuous_scale=\"Emrld\", title='COVID-19')\n",
    "fig.update(layout_coloraxis_showscale=False)\n",
    "fig.write_image('covid-compare-1-1.png')\n",
    "\n",
    "#-----------------------------------------------------------------------------------------\n",
    "\n",
    "fig = px.choropleth(e_lat_grp, locations=\"Country\", locationmode='country names',\n",
    "                    color=\"Cases\", hover_name=\"Country\", \n",
    "                    color_continuous_scale=\"Emrld\", title='EBOLA 2014')\n",
    "fig.update(layout_coloraxis_showscale=False)\n",
    "fig.write_image('covid-compare-1-2.png')\n",
    "\n",
    "#-----------------------------------------------------------------------------------------\n",
    "\n",
    "fig = px.choropleth(s_lat_grp, locations=\"Country\", locationmode='country names',\n",
    "                    color=\"Cases\", hover_name=\"Country\", \n",
    "                    color_continuous_scale=\"Emrld\", title='SARS 2003')\n",
    "fig.update(layout_coloraxis_showscale=False)\n",
    "fig.write_image('covid-compare-1-3.png')\n",
    "\n",
    "#-----------------------------------------------------------------------------------------\n",
    "\n",
    "fig = px.choropleth(mers_cntry, locations=\"Country\", locationmode='country names',\n",
    "                    color=\"Confirmed\", hover_name=\"Country\", \n",
    "                    color_continuous_scale='Emrld', title='MERS')\n",
    "fig.update(layout_coloraxis_showscale=False)\n",
    "fig.write_image('covid-compare-1-4.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.022879,
     "end_time": "2020-04-17T06:07:36.390304",
     "exception": false,
     "start_time": "2020-04-17T06:07:36.367425",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "![](covid-compare-1-1.png)\n",
    "![](covid-compare-1-2.png)\n",
    "![](covid-compare-1-3.png)\n",
    "![](covid-compare-1-4.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.023019,
     "end_time": "2020-04-17T06:07:36.437502",
     "exception": false,
     "start_time": "2020-04-17T06:07:36.414483",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Deaths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-04-17T06:07:36.506371Z",
     "iopub.status.busy": "2020-04-17T06:07:36.505781Z",
     "iopub.status.idle": "2020-04-17T06:07:37.745259Z",
     "shell.execute_reply": "2020-04-17T06:07:37.744333Z"
    },
    "papermill": {
     "duration": 1.283666,
     "end_time": "2020-04-17T06:07:37.745383",
     "exception": false,
     "start_time": "2020-04-17T06:07:36.461717",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#hide\n",
    "fig = px.choropleth(c_lat_grp[c_lat_grp['Deaths']>0], locations=\"Country\", locationmode='country names',\n",
    "                    color=\"Deaths\", hover_name=\"Country\", \n",
    "                    color_continuous_scale=\"Sunsetdark\", title='COVID-19')\n",
    "fig.update(layout_coloraxis_showscale=False)\n",
    "fig.write_image('covid-compare-2-1.png')\n",
    "\n",
    "#-----------------------------------------------------------------------------------------\n",
    "\n",
    "fig = px.choropleth(e_lat_grp[e_lat_grp['Deaths']>0], locations=\"Country\", locationmode='country names',\n",
    "                    color=\"Deaths\", hover_name=\"Country\", \n",
    "                    color_continuous_scale=\"Sunsetdark\", title='EBOLA 2014')\n",
    "fig.update(layout_coloraxis_showscale=False)\n",
    "fig.write_image('covid-compare-2-2.png')\n",
    "\n",
    "\n",
    "#-----------------------------------------------------------------------------------------\n",
    "\n",
    "fig = px.choropleth(s_lat_grp[s_lat_grp['Deaths']>0], locations=\"Country\", locationmode='country names',\n",
    "                    color=\"Deaths\", hover_name=\"Country\", \n",
    "                    color_continuous_scale=\"Sunsetdark\", title='SARS 2003')\n",
    "fig.update(layout_coloraxis_showscale=False)\n",
    "# fig.write_image('covid-compare-2-3.png')\n",
    "fig.write_image('covid-compare-2-3.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.024613,
     "end_time": "2020-04-17T06:07:37.798004",
     "exception": false,
     "start_time": "2020-04-17T06:07:37.773391",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "![](covid-compare-2-1.png)\n",
    "![](covid-compare-2-2.png)\n",
    "![](covid-compare-2-3.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.023222,
     "end_time": "2020-04-17T06:07:37.845252",
     "exception": false,
     "start_time": "2020-04-17T06:07:37.822030",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Proportion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-04-17T06:07:37.926416Z",
     "iopub.status.busy": "2020-04-17T06:07:37.925757Z",
     "iopub.status.idle": "2020-04-17T06:07:40.001929Z",
     "shell.execute_reply": "2020-04-17T06:07:40.000923Z"
    },
    "papermill": {
     "duration": 2.13283,
     "end_time": "2020-04-17T06:07:40.002055",
     "exception": false,
     "start_time": "2020-04-17T06:07:37.869225",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#hide\n",
    "fig = px.treemap(c_lat_grp.sort_values(by='Cases', ascending=False).reset_index(drop=True), \n",
    "                 path=[\"Country\"], values=\"Cases\", title='COVID-19',\n",
    "                 color_discrete_sequence = px.colors.qualitative.Dark2)\n",
    "fig.write_image('covid-compare-3-1.png')\n",
    "\n",
    "fig = px.treemap(e_lat_grp.sort_values(by='Cases', ascending=False).reset_index(drop=True), \n",
    "                 path=[\"Country\"], values=\"Cases\", title='EBOLA',\n",
    "                 color_discrete_sequence = px.colors.qualitative.Dark2)\n",
    "fig.write_image('covid-compare-3-2.png')\n",
    "\n",
    "fig = px.treemap(s_lat_grp.sort_values(by='Cases', ascending=False).reset_index(drop=True), \n",
    "                 path=[\"Country\"], values=\"Cases\", title='SARS',\n",
    "                 color_discrete_sequence = px.colors.qualitative.Dark2)\n",
    "fig.write_image('covid-compare-3-3.png')\n",
    "\n",
    "fig = px.treemap(mers_cntry, \n",
    "                 path=[\"Country\"], values=\"Confirmed\", title='MERS',\n",
    "                 color_discrete_sequence = px.colors.qualitative.Dark2)\n",
    "fig.write_image('covid-compare-3-4.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.025566,
     "end_time": "2020-04-17T06:07:40.051880",
     "exception": false,
     "start_time": "2020-04-17T06:07:40.026314",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "![](covid-compare-3-1.png)\n",
    "![](covid-compare-3-2.png)\n",
    "![](covid-compare-3-3.png)\n",
    "![](covid-compare-3-4.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.025708,
     "end_time": "2020-04-17T06:07:40.103518",
     "exception": false,
     "start_time": "2020-04-17T06:07:40.077810",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-04-17T06:07:40.163700Z",
     "iopub.status.busy": "2020-04-17T06:07:40.162478Z",
     "iopub.status.idle": "2020-04-17T06:07:40.164418Z",
     "shell.execute_reply": "2020-04-17T06:07:40.164878Z"
    },
    "papermill": {
     "duration": 0.037361,
     "end_time": "2020-04-17T06:07:40.165002",
     "exception": false,
     "start_time": "2020-04-17T06:07:40.127641",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#hide\n",
    "# sum of cases\n",
    "# -----------\n",
    "\n",
    "c_cases = sum(c_lat_grp['Cases'])\n",
    "c_deaths = sum(c_lat_grp['Deaths'])\n",
    "c_no_countries = len(c_lat_grp['Country'].value_counts())\n",
    "\n",
    "s_cases = sum(s_lat_grp['Cases'])\n",
    "s_deaths = sum(s_lat_grp['Deaths'])\n",
    "s_no_countries = len(s_lat_grp['Country'].value_counts())\n",
    "\n",
    "e_cases = sum(e_lat_grp['Cases'])\n",
    "e_deaths = sum(e_lat_grp['Deaths'])\n",
    "e_no_countries = len(e_lat_grp['Country'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-04-17T06:07:40.233915Z",
     "iopub.status.busy": "2020-04-17T06:07:40.233249Z",
     "iopub.status.idle": "2020-04-17T06:07:40.238562Z",
     "shell.execute_reply": "2020-04-17T06:07:40.237986Z"
    },
    "papermill": {
     "duration": 0.048856,
     "end_time": "2020-04-17T06:07:40.238666",
     "exception": false,
     "start_time": "2020-04-17T06:07:40.189810",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>epidemic</th>\n",
       "      <th>start_year</th>\n",
       "      <th>end_year</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>deaths</th>\n",
       "      <th>no_of_countries</th>\n",
       "      <th>mortality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SARS</td>\n",
       "      <td>2003</td>\n",
       "      <td>2004</td>\n",
       "      <td>8432</td>\n",
       "      <td>813</td>\n",
       "      <td>31</td>\n",
       "      <td>9.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>H1N1</td>\n",
       "      <td>2009</td>\n",
       "      <td>2010</td>\n",
       "      <td>6724149</td>\n",
       "      <td>19654</td>\n",
       "      <td>178</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EBOLA</td>\n",
       "      <td>2014</td>\n",
       "      <td>2016</td>\n",
       "      <td>28642</td>\n",
       "      <td>11319</td>\n",
       "      <td>10</td>\n",
       "      <td>39.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MERS</td>\n",
       "      <td>2012</td>\n",
       "      <td>2017</td>\n",
       "      <td>2494</td>\n",
       "      <td>858</td>\n",
       "      <td>27</td>\n",
       "      <td>34.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>COVID-19</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>529591</td>\n",
       "      <td>23970</td>\n",
       "      <td>175</td>\n",
       "      <td>4.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   epidemic  start_year  end_year  confirmed  deaths  no_of_countries  \\\n",
       "0      SARS        2003      2004       8432     813               31   \n",
       "1      H1N1        2009      2010    6724149   19654              178   \n",
       "2     EBOLA        2014      2016      28642   11319               10   \n",
       "3      MERS        2012      2017       2494     858               27   \n",
       "4  COVID-19        2019      2020     529591   23970              175   \n",
       "\n",
       "   mortality  \n",
       "0       9.64  \n",
       "1       0.29  \n",
       "2      39.52  \n",
       "3      34.40  \n",
       "4       4.53  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide\n",
    "epidemics = pd.DataFrame({\n",
    "    'epidemic' : ['COVID-19', 'SARS', 'EBOLA', 'MERS', 'H1N1'],\n",
    "    'start_year' : [2019, 2003, 2014, 2012, 2009],\n",
    "    'end_year' : [2020, 2004, 2016, 2017, 2010],\n",
    "    'confirmed' : [c_cases, s_cases, e_cases, 2494, 6724149],\n",
    "    'deaths' : [c_deaths, s_deaths, e_deaths, 858, 19654],\n",
    "    'no_of_countries' : [c_no_countries, s_no_countries, e_no_countries, 27, 178]\n",
    "})\n",
    "\n",
    "epidemics['mortality'] = round((epidemics['deaths']/epidemics['confirmed'])*100, 2)\n",
    "epidemics = epidemics.sort_values('end_year').reset_index(drop=True)\n",
    "epidemics.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-04-17T06:07:40.317045Z",
     "iopub.status.busy": "2020-04-17T06:07:40.313292Z",
     "iopub.status.idle": "2020-04-17T06:07:40.768026Z",
     "shell.execute_reply": "2020-04-17T06:07:40.767389Z"
    },
    "papermill": {
     "duration": 0.501788,
     "end_time": "2020-04-17T06:07:40.768143",
     "exception": false,
     "start_time": "2020-04-17T06:07:40.266355",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#hide\n",
    "fig = px.bar(epidemics.sort_values('confirmed',ascending=False), \n",
    "             x=\"confirmed\", y=\"epidemic\", color='epidemic', \n",
    "             text='confirmed', orientation='h', title='No. of Cases', \n",
    "             range_x=[0,7500000],\n",
    "             color_discrete_sequence = [h, c, e, s, m])\n",
    "fig.update_traces(textposition='outside')\n",
    "fig.update_layout(uniformtext_minsize=8, uniformtext_mode='hide')\n",
    "fig.write_image('covid-compare-4-1.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-04-17T06:07:40.842120Z",
     "iopub.status.busy": "2020-04-17T06:07:40.839204Z",
     "iopub.status.idle": "2020-04-17T06:07:41.220636Z",
     "shell.execute_reply": "2020-04-17T06:07:41.219656Z"
    },
    "papermill": {
     "duration": 0.426087,
     "end_time": "2020-04-17T06:07:41.220765",
     "exception": false,
     "start_time": "2020-04-17T06:07:40.794678",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#hide\n",
    "fig = px.bar(epidemics.sort_values('deaths',ascending=False), \n",
    "             x=\"deaths\", y=\"epidemic\", color='epidemic', \n",
    "             text='deaths', orientation='h', title='No. of Deaths',\n",
    "             range_x=[0,25000],\n",
    "             color_discrete_sequence = [h, e, c, m, s])\n",
    "fig.update_traces(textposition='outside')\n",
    "fig.update_layout(uniformtext_minsize=8, uniformtext_mode='hide')\n",
    "fig.write_image('covid-compare-4-2.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-04-17T06:07:41.304773Z",
     "iopub.status.busy": "2020-04-17T06:07:41.286319Z",
     "iopub.status.idle": "2020-04-17T06:07:41.851800Z",
     "shell.execute_reply": "2020-04-17T06:07:41.850644Z"
    },
    "papermill": {
     "duration": 0.601215,
     "end_time": "2020-04-17T06:07:41.851935",
     "exception": false,
     "start_time": "2020-04-17T06:07:41.250720",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#hide\n",
    "fig = px.bar(epidemics.sort_values('mortality',ascending=False),\n",
    "             x=\"mortality\", y=\"epidemic\", color='epidemic', \n",
    "             text='mortality', orientation='h', title='Mortality rate', \n",
    "             range_x=[0,100],\n",
    "             color_discrete_sequence = [e, m, s, c, h])\n",
    "fig.update_traces(textposition='outside')\n",
    "fig.update_layout(uniformtext_minsize=8, uniformtext_mode='hide')\n",
    "fig.write_image('covid-compare-4-3.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-04-17T06:07:41.957811Z",
     "iopub.status.busy": "2020-04-17T06:07:41.931189Z",
     "iopub.status.idle": "2020-04-17T06:07:42.296118Z",
     "shell.execute_reply": "2020-04-17T06:07:42.295313Z"
    },
    "papermill": {
     "duration": 0.418662,
     "end_time": "2020-04-17T06:07:42.296235",
     "exception": false,
     "start_time": "2020-04-17T06:07:41.877573",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#hide\n",
    "fig = px.bar(epidemics.sort_values('no_of_countries', ascending=False),\n",
    "             x=\"no_of_countries\", y=\"epidemic\", color='epidemic', \n",
    "             text='no_of_countries', orientation='h', title='No. of Countries', \n",
    "             range_x=[0,200],\n",
    "             color_discrete_sequence = [h, c, s, m, e])\n",
    "fig.update_traces(textposition='outside')\n",
    "fig.update_layout(uniformtext_minsize=8, uniformtext_mode='hide')\n",
    "fig.write_image('covid-compare-4-4.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-04-17T06:07:42.368535Z",
     "iopub.status.busy": "2020-04-17T06:07:42.353864Z",
     "iopub.status.idle": "2020-04-17T06:07:43.308613Z",
     "shell.execute_reply": "2020-04-17T06:07:43.306846Z"
    },
    "papermill": {
     "duration": 0.986913,
     "end_time": "2020-04-17T06:07:43.308738",
     "exception": false,
     "start_time": "2020-04-17T06:07:42.321825",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#hide\n",
    "temp = pd.concat([s_dbd, e_dbd, c_dbd], axis=0, sort=True)\n",
    "                \n",
    "fig = px.line(temp, x=\"Date\", y=\"Cases\", color='epidemic', \n",
    "             title='No. of new cases',\n",
    "             color_discrete_sequence = sec)\n",
    "fig.update_layout(xaxis_rangeslider_visible=True)\n",
    "fig.write_image('covid-compare-4-5.png')\n",
    "\n",
    "fig = px.line(temp, x=\"Date\", y=\"Deaths\", color='epidemic', \n",
    "             title='No. of new deaths',\n",
    "             color_discrete_sequence = sec)\n",
    "fig.update_layout(xaxis_rangeslider_visible=True)\n",
    "fig.write_image('covid-compare-4-6.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.024575,
     "end_time": "2020-04-17T06:07:43.359260",
     "exception": false,
     "start_time": "2020-04-17T06:07:43.334685",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "![](covid-compare-4-1.png)\n",
    "![](covid-compare-4-2.png)\n",
    "![](covid-compare-4-3.png)\n",
    "![](covid-compare-4-4.png)\n",
    "![](covid-compare-4-5.png)\n",
    "![](covid-compare-4-6.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.021966,
     "end_time": "2020-04-17T06:07:43.403253",
     "exception": false,
     "start_time": "2020-04-17T06:07:43.381287",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# In the first N days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-04-17T06:07:43.462980Z",
     "iopub.status.busy": "2020-04-17T06:07:43.462384Z",
     "iopub.status.idle": "2020-04-17T06:07:44.769343Z",
     "shell.execute_reply": "2020-04-17T06:07:44.768188Z"
    },
    "papermill": {
     "duration": 1.344009,
     "end_time": "2020-04-17T06:07:44.769475",
     "exception": false,
     "start_time": "2020-04-17T06:07:43.425466",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#hide\n",
    "fig = px.line(temp, x=\"nth_day\", y=\"Cases\", color='epidemic', \n",
    "             title='Cases', color_discrete_sequence = sec)\n",
    "fig.update_layout(xaxis_rangeslider_visible=True)\n",
    "fig.write_image('covid-compare-5-1.png')\n",
    "\n",
    "fig = px.line(temp, x=\"nth_day\", y=\"Deaths\", color='epidemic', \n",
    "             title='Deaths', color_discrete_sequence = sec)\n",
    "fig.update_layout(xaxis_rangeslider_visible=True)\n",
    "fig.write_image('covid-compare-5-2.png')\n",
    "\n",
    "fig = px.line(temp, x=\"nth_day\", y=\"n_countries\", color='epidemic', \n",
    "             title='No. of Countries', color_discrete_sequence = sec)\n",
    "fig.update_layout(xaxis_rangeslider_visible=True)\n",
    "fig.write_image('covid-compare-5-3.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-04-17T06:07:44.831927Z",
     "iopub.status.busy": "2020-04-17T06:07:44.831075Z",
     "iopub.status.idle": "2020-04-17T06:07:45.708442Z",
     "shell.execute_reply": "2020-04-17T06:07:45.708895Z"
    },
    "papermill": {
     "duration": 0.91356,
     "end_time": "2020-04-17T06:07:45.709037",
     "exception": false,
     "start_time": "2020-04-17T06:07:44.795477",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#hide\n",
    "fig = px.scatter(epidemics, x='start_year', y = [1 for i in range(len(epidemics))], \n",
    "                 size=epidemics['confirmed']**0.3, color='epidemic', title='Confirmed Cases',\n",
    "                 color_discrete_sequence = shemc, hover_name='epidemic', height=400,\n",
    "                 text=epidemics['epidemic']+'<br> Cases : '+epidemics['confirmed'].apply(str))\n",
    "fig.update_traces(textposition='bottom center')\n",
    "fig.update_yaxes(showticklabels=False)\n",
    "fig.update_layout(showlegend=False)\n",
    "fig.write_image('covid-compare-5-4.png')\n",
    "\n",
    "fig = px.scatter(epidemics, x='start_year', y = [1 for i in range(len(epidemics))], \n",
    "                 size=epidemics['deaths']**0.5, color='epidemic', title='Deaths',\n",
    "                 color_discrete_sequence = shemc, hover_name='epidemic', height=400,\n",
    "                 text=epidemics['epidemic']+'<br> Deaths : '+epidemics['deaths'].apply(str))\n",
    "fig.update_traces(textposition='bottom center')\n",
    "fig.update_yaxes(showticklabels=False)\n",
    "fig.update_layout(showlegend=False)\n",
    "fig.write_image('covid-compare-5-5.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.025585,
     "end_time": "2020-04-17T06:07:45.759860",
     "exception": false,
     "start_time": "2020-04-17T06:07:45.734275",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "![](covid-compare-5-1.png)\n",
    "![](covid-compare-5-2.png)\n",
    "![](covid-compare-5-3.png)\n",
    "![](covid-compare-5-4.png)\n",
    "![](covid-compare-5-5.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-04-17T06:07:45.819780Z",
     "iopub.status.busy": "2020-04-17T06:07:45.819165Z",
     "iopub.status.idle": "2020-04-17T06:07:45.824277Z",
     "shell.execute_reply": "2020-04-17T06:07:45.824690Z"
    },
    "papermill": {
     "duration": 0.040222,
     "end_time": "2020-04-17T06:07:45.824813",
     "exception": false,
     "start_time": "2020-04-17T06:07:45.784591",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Cases</th>\n",
       "      <th>Deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>94</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>174</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>367</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>224</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Cases  Deaths\n",
       "0  Afghanistan     94       4\n",
       "1      Albania    174       6\n",
       "2      Algeria    367      25\n",
       "3      Andorra    224       3\n",
       "4       Angola      4       0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide\n",
    "c_lat_grp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-04-17T06:07:45.889534Z",
     "iopub.status.busy": "2020-04-17T06:07:45.888949Z",
     "iopub.status.idle": "2020-04-17T06:07:46.159179Z",
     "shell.execute_reply": "2020-04-17T06:07:46.160193Z"
    },
    "papermill": {
     "duration": 0.30864,
     "end_time": "2020-04-17T06:07:46.160388",
     "exception": false,
     "start_time": "2020-04-17T06:07:45.851748",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABI8AAAHdCAYAAACUi1lZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzdeVRV5eL/8c9hOIIDKipgYpqaSprglIpSDqho2mCOJc22zKFugxM5oF0lSyy1NNNKc76pmVcNZ0zFmUQt8IbTxYFJZVKQ4ZzfH/0837iIaKGHA+/XWq0V+9ln788+D61172c9z8ZgNpvNAgAAAAAAAG7BztoBAAAAAAAAUHJRHgEAAAAAAKBQlEcAAAAAAAAoFOURAAAAAAAACkV5BAAAAAAAgEJRHgEAAAAAAKBQlEcAAMAmNGrUSEFBQfmOHThwQIGBgcV2j86dO+vw4cPFdr3bSU9P19NPP61u3brp6tWrBcZPnTql4cOHy9/fX127dtWAAQO0c+fO+5INAADgzyiPAACAzTh06JB+++03a8coFidPnlRKSoq2bNmiqlWr5htLSEjQ4MGD5efnp61bt2rr1q0aO3asxo0bpz179lgpMQAAKKsojwAAgM149913NW3atFuOzZkzRx988MEtfw4MDNRXX32lAQMGqG3btlq2bJnmzp2rgIAA9ezZU3FxcZbP7d+/X88884yeeOIJffrpp5bj27ZtU+/evdWlSxe9+uqrunLliuU+48ePV9++fbVo0aICuQ4cOKBnn31WAQEB6tevn44fP66LFy/q/fff1+XLlxUQEGC51k2LFi2Sr6+vBg4cKIPBIElq3ry55s6dq3r16kmStm/frt69e6t79+7q06ePoqOjJUnXrl3T8OHD1aNHD3Xp0kXjx49XTk6OJGnVqlUKCAhQ586d9e677yorK0uSdPDgQT377LPq2bOnevTooZ9++unOJwUAAJR6lEcAAMBm9OjRQ2azWWFhYXf92UOHDmnZsmUKCQnRJ598Ig8PD4WFhalBgwZas2aN5bxff/1Va9as0dq1a7VixQrFxMQoLi5Oo0ePVmhoqLZv3642bdooODjY8pldu3bpq6++0ssvv5zvnteuXdPbb7+t8ePHKywsTK+//rref/99eXh4aPr06apZs6bCwsLk6upaIOsTTzxR4BlatGihBx54QLm5uRo7dqw+/PBDbd68WZ07d9b06dMlSevWrZOLi4t++uknbd68Wfb29oqNjdXhw4c1a9YsLV68WDt27FDFihU1a9YsSdL06dM1btw4bdq0SfPmzdO2bdvu+vsFAAClF+URAACwKUFBQZoxY4Zu3LhxV5/r1KmTHBwc1LBhQ2VmZqp79+6SpIYNGyoxMdFyXu/evWVvb69q1aqpdevW+uWXX/Tzzz/rscceU8OGDSVJAwcO1I4dO5SXlydJ8vb2LlAASdKxY8fk4eGhli1bSpK6d++uq1ev6sKFC7fNmpqaqurVqxc67uDgoIiICPn4+EiSWrVqZVk95erqql9++UV79uyRyWTS5MmT5eXlpR07dqhnz55yd3eXJA0aNEhbtmyRJFWrVk3r1q3TqVOnVLduXYWGhhb9hQIAgDLDwdoBAAAA7kaTJk3UunVrffvtt2revPkdf65ChQqSJHt7+3w/29nZyWQyWc77cwlUqVIlpaWlyWw26/DhwwoICLCMVaxYUSkpKZKkypUr3/KeV65ckYuLS75jlSpV0uXLl2+btWrVqkpISLjtOUuWLNEPP/yg7OxsZWdnW7a39ejRQ6mpqZo1a5ZOnz6tp556SuPGjVN6erq2bt1qeWeS2Wy2bGebNm2a5s2bp1deeUVOTk5699138z0rAAAo2yiPAACAzXnnnXfUp08feXp6Wo79bwmUmpr6l67958+lpqaqcuXKMhqN8vX11ezZs+/qWtWqVbMUTNIfhU1qaqqqVaumixcvFvq5Nm3aaPPmzXr22WfzHd++fbvKlSun8uXLa8GCBfr+++/l6empvXv3asKECZbzBg4cqIEDByohIUEjR47UunXr5ObmpmeffVZjxowpcL/q1atrwoQJmjBhgvbs2aORI0fKz8/PUrABAICyjW1rAADA5ri5uemFF17QnDlz8h37z3/+I5PJpCtXrujnn3/+S9feuHGjTCaTLl++rCNHjqhVq1bq0KGDDh8+bNkaduzYMf3zn/8s8lrNmjVTcnKyfvnlF8u1PTw88pVet/LSSy/p+PHj+uqrryyF2JEjRzRp0iQ5OTnpypUrqlatmh544AFlZmbqhx9+0PXr12U2m/XFF19o9erVkiR3d3d5enrKYDCoc+fO2rJli+Xl3Nu2bdNXX32lnJwcBQYGWrbuNWnSRA4ODrKz438mAgCAP7DyCAAA2KRXX31V33//veXngIAArV+/Xv7+/qpXr54CAgKK3B52K48++qj69u2rK1eu6KWXXlKDBg0kSR9++KGGDx+unJwcVahQQUFBQUVeq3z58vrss8/04Ycf6vr163J1ddXMmTMtW8wKU716dS1fvlwff/yx/P39Va5cOdWoUUOfffaZWrVqpRs3bmj58uXy9/eXu7u7goKCFBUVpbfeektjxozRuHHjtGDBAhkMBnl7e+vpp5+W0WjU0KFDFRgYKJPJpGrVqmny5MlydHRU3759LS/7trOz0/jx4+Xs7HzX3x0AACidDGaz2WztEAAAAAAAACiZWI8MAAAAAACAQlEeAQAAAAAAoFCURwAAAAAAACgU5REAAAAAAAAKZXN/be3IkSPWjgAAAAAAAFDqtGzZ8pbHba48kgp/GFsTHR0tLy8va8dAGcTvnu1i7mwXc2e7mLuyiXm3Xcyd7WLubBdzVzrcbrEO29YAAAAAAABQKMojAAAAAAAAFIryCAAAAAAAAIWiPAIAAAAAAEChKI8AAAAAAABQKMojAAAAAAAAFIryCAAAAAAAAIWiPAIAAAAAAEChHKwdoLQ4e/aspk2bpitXrshkMql58+YaM2aMjEajMjMzFRISomPHjsnBwUHVq1fXpEmTJEkDBgzQhAkT1LRpU8u1QkNDVbVqVV27dk1Vq1bV4MGDFRgYqOvXr6t8+fLKyclR+/btNWzYMNnb2xfI8p///EfDhg3Tyy+/rMGDB0uSTp06pYkTJ8pgMKhu3boKDg6Wg0P+6b927ZrefPNNzZkzR9HR0Zo5c6bs7Oz00EMPaerUqbKzs9O0adMUFRUlg8GgoKAgNWvWTJL03Xffafr06Tp48KAqVKggSVq5cqW+//57OTo66pVXXlH37t0LZN20aZO++eYb2dnZqV27dnrnnXeUk5OjsWPH6uLFi7K3t1dISIhq166tmJgYTZkyRXZ2dnJxcVFoaKicnZ21cOFChYWFyWAwaMSIEerQoYPeeOMNTZkyRbVq1SqeCQYAAAAAoIxi5VExyMvL08iRI/X6669r9erVWrNmjSTpiy++kCSFhITIzc1N69at0+rVqzVkyBC9/vrrys3NVa9evfTTTz/lu96WLVv05JNPFrhPSEiIlixZou+++06JiYn69NNPC5xz/fp1ffjhh2rXrl2+4zNmzNAbb7yhpUuXqmbNmgXuKUmff/65+vfvr8qVK2vixImaPXu2Vq5cqWvXrmn37t06ePCgzp07p1WrVmnq1KmaOnWqJGndunW6fPmy3NzcLNe6fPmyvvnmGy1fvlyLFy/Wt99+q6ysrHz3y8zM1IwZM7Ro0SKtWrVKERERio2N1YYNG+Ti4qIVK1Zo6NChCg0NlST985//1NixY7V06VLVqVNHa9euVVxcnDZt2qTly5dr/vz5CgkJkSS99957+vDDD28/cQAAAAAAoEiUR8Vg7969qlevnh577DFJksFg0KhRozR8+HBlZGRo9+7dGjp0qOX8li1bqlmzZjp48KB69uyprVu3WsZOnDghNzc3ubu7F3o/o9GocePGaf369crJySkwtmDBgnxFjiSdO3fOskrIz89Pe/fuzTd+48YNbd68WT169JAkrV27Vh4eHpIkV1dXXb16Vfv27ZO/v78kqX79+kpNTVVGRob8/f31zjvvyGAwWK534cIF1atXT+XKlVO5cuXUuHFjRUVF5buns7Oz1q9fr4oVK8pgMKhKlSpKSUnRvn371LVrV0mSr6+vIiMjJUlffvml5RlcXV2VkpKiAwcOyM/PT0ajUa6urqpVq5ZiY2P1yCOPKDU1VefOnSv0ewQAAAAAAEUrddvWIiMjdejQoWK9ZuvWrdWiRYtCx0+fPi0vL698x5ycnCT9sV2sXr16BbaIeXl56ezZs6pWrZpq166tY8eOqVmzZvrpp5/Uu3fvIjOVL19eNWvW1KVLl/Tggw9ajjs4OBS4lyQ1bNhQu3bt0jPPPKPdu3crOTk53/ixY8fUsGFDyza4ihUrSpISExO1d+9evf3225o5c6aaNGli+Yyrq6uSkpL00EMPFbjfgw8+qP/85z+6cuWKypUrp19++cVSrv3ZzfucPHlSFy5ckLe3t+bOnStXV1dJkp2dnQwGg7Kzsy3nXr9+XT/++KNmzZqlrVu3Ws79c6ZGjRqpdevWOnDggOrUqVPk9wkAAAAAAG6NlUfFwGAwKC8v767GzGaz7Oz++Pp79eqlTZs2SZJ27Nhxy3cD3cq1a9cs1yjKmDFj9NNPP+nFF1+U2WyW2WzON56YmGhZaXTT5cuXNXToUE2aNElVq1a95TMUpkqVKho1apSGDRumsWPHqkGDBoWef/bsWb3//vsKDQ2Vo6Pjbe9z/fp1vfnmm3r11VdVv379257r7u6uS5cuFZoRAAAAAAAUrdStPGrRosVtVwndC/Xq1dOyZcvyHcvOztbZs2fl6empM2fOKDs7W0aj0TIeExOjxo0bS5K6du2qL7/8Uk8++aTq1q2rypUrF3nP1NRUpaWl6YEHHtCbb76pjIwMPfXUU+rXr98tz69Zs6bmz58vSdq9e7cSExMLnPPnbWcZGRkaMmSI/vGPf6hDhw6SJDc3t3wrlhITE1WjRo1CM/bo0cOyDe7dd99VrVq1NHv2bB06dEgNGzbUhAkTFB8fr+HDh+vjjz+2rN5yc3NTUlKSGjdurJycHJnNZhmNRuXm5mrYsGHq1auX+vTpYzn3zJkzlnsmJCQU2LIHAAAAAAD+OlYeFYP27dvrwoUL2rFjhyTJZDLpk08+0aZNm1SxYkV16tRJn3/+ueX8yMhI/fbbb2rZsqWkP7ZuNWrUSPPnz7+jLWu5ubmaNm2aXnzxRdnZ2WnevHlasmRJocWRJM2ePVvh4eGS/nifUefOnfONu7m5KT4+3vLzRx99pJdeekmPP/54vufcvHmzJOnXX3+Vm5ubZSvZrTIGBgbqxo0bSkpKUnR0tJo2baq33npLS5Ys0YQJEyRJH3zwgYKDg/Nth2vfvr3CwsIkSTt37lSbNm0kSQsWLNBjjz2W7znbtm2r8PBwZWdnKyEhQYmJiWrQoIGkW6+mAgAAAAAAd6fUrTyyBjs7O3399deaOHGiPv/8cxmNRvn6+mrEiBGSpKCgIIWGhuqpp56yvNh51qxZyszMtFyjd+/eGj16tGbMmFHofcaNGydnZ2elpqaqY8eOeuWVVwqcc+LECU2fPl0XLlyQg4ODNm/erDlz5qhXr14aPXq05syZo1atWqljx475PtesWTOdPHlSeXl5ys7O1rp163Tu3DmtXr1a0h9b6wYMGKAmTZpo4MCBMhgMmjRpkiRp3rx5ioiIUFJSkoYMGSIfHx+NHj1aAQEBGjBggAwGgyZOnFjgXUxnzpzR4cOHNXv2bMuxl19+WT179lRERIQGDRoko9Gojz76SJK0bNkyeXp6at++fZKkNm3aaMSIEerfv78GDx4sg8Gg4OBgy1a+Q4cOadq0aXc0hwAAAAAA4NYM5tu9uKYEOnLkiGXFjq2Ljo4u8KJtawoJCZG3t7d69uxp7Sh/W0xMjGbOnKmvvvrK2lFKpJL2u4c7x9zZLubOdjF3ZRPzbruYO9vF3Nku5q50uF3fwrY1WIwcOVKrVq1SamqqtaP8LXl5efrkk08sW+MAAAAAAChuZrNZhw8fVkpKirWj3HOUR7CoWLGiFi9efEcv7C7J7O3t9fXXX6t27drWjgIAAAAAKKXi4+O1evVqnT592tpR7jnKIwAAAAAAgLsUFRUlg8Gghg0bWjvKPUd5BAAAAAAAcBfMZrOioqLUoEGDQv8KeWlCeQQAAAAAAHAXLly4oKtXr8rb29vaUe4LyiMAAAAAAIC7cPToUdnb2+uRRx6xdpT7wsHaAUqLZcuW6ccff5TRaFRWVpbeffdd+fr6WsZfe+01lStXTnPnzrUcGzJkiB588EHZ29vLZDLJyclJ06ZNk7u7u+Lj4zVhwgRlZmYqKytLDz/8sCZPniyj0ZjvvgkJCRo7dqzmz5+vbdu26ZtvvpGdnZ3atWund955Rzk5ORo7dqwuXrwoe3t7hYSEqHbt2oqJiVFwcLAkqVGjRpo8ebJMJpOmTJmikydPKjc3V/3791e/fv0KPOvixYv173//W2azWX369NELL7yg9PR0vffee0pPT1f58uUVGhqqKlWqKCIiQjNnzpS9vb0ef/xxDR8+3HKdrKws9erVS8OGDVO3bt305ptvau7cuapUqVIxzw4AAAAAAMXDZDLp+PHjatiwocqXL2/tOPcFK4+Kwfnz5/Wvf/1Ly5Yt09KlSzVjxox8JdHly5d16tQpRUZGKj09Pd9nFyxYoCVLlmjZsmV68sknNWvWLEnSrFmz1KdPHy1dulSrV6+Wo6Ojdu/eXeDeU6dO1ciRI5WXl6cZM2Zo0aJFWrVqlSIiIhQbG6sNGzbIxcVFK1as0NChQxUaGmr5XFBQkFauXKmMjAzt2rVLkZGRcnBw0IoVK7Ro0SLNnDlTJpMp3/3i4uK0du1arVy5UitWrNDXX3+t9PR0LV68WI899phWrFihbt26acGCBZKkf/7zn5ozZ45WrFihvXv3KjY21nKtefPmWf6yW8WKFfXiiy/q008/LYYZAQAAAADg3vjvf/+r1NTUMrNlTaI8KhYZGRm6ceOGcnJyJEl169bV0qVLLeObNm1Sp06d1L59e23ZsqXQ63h7e+vcuXOSpLS0NGVkZFjGpkyZoi5duuQ7/+LFi4qLi1OLFi3k7Oys9evXq2LFijIYDKpSpYpSUlK0b98+de3aVZLk6+uryMhIZWdn68KFC2rWrJkkqVOnTtq3b59atWql8ePHS5KuXLmiypUry84u/69IrVq1tHz5cjk4OMhoNMrJyUkZGRn57nPzenFxcapcubJq1qwpOzs7PfHEE9q3b58k6dSpU4qNjVXHjh0t1/b399fevXt17dq1O//yAQAAAAC4j6KiouTg4CAvLy9rR7lvSuW2tdQx0wocK+fXRk69usicdUNpk0ILjvv7yamrn0yp6UqfNiffWOXpQbe9X+PGjdWsWTN16dJFTzzxhB5//HF169ZNDg5/fL0bNmzQqFGjlJ6erqVLl+q555675XXCwsIs+yWHDBmiYcOGae3atWrfvr169+6tOnXq5Dv/4MGDatmypeXnm294P3nypC5cuCBvb2/NnTtXrq6ukiQ7OzsZDAYlJyfLxcXF8rlq1aopKSnJ8vNbb72lyMhIffLJJwUy2tnZqUKFCpKkPXv2qGrVqqpZs6aSk5Mt96lWrZoSExOVlJRkOSZJrq6uiouLkyRNnz5dEyZM0Lp16yzjBoNBTZs21dGjR9W+ffvbfucAAAAAANxvN7esNW7cWOXKlbN2nPuGlUfF5OOPP9bSpUvVuHFjLVy4UK+88orMZrPi4uKUkJCgli1bqkOHDoqJidGVK1csnxsyZIgCAwPVsWNHnT59Wm+//bYkycfHR9u3b9drr72mxMRE9e3bV3v27Ml3z8TERLm7u+c7dvbsWb3//vsKDQ2Vo6NjgZxms7nIY7Nnz9aqVas0efLkfKuf/uzo0aOaPn26ZsyYcUf3+LN169bJx8dHtWvXLjDm7u6uS5cu3fbzAAAAAABYw+nTp5WRkSEfHx9rR7mvSuXKo9utFDI4lbvtuF3lSkWuNPpfZrNZ2dnZql+/vurXr6/AwED16NFDFy9e1IYNG3Tjxg0988wzkqTc3Fz99NNPeuGFFyT98c6jChUqaOnSpTp79qxl9VBWVpacnZ3l7+8vf39/NW/eXBs3blSHDh3yP4/BYPn3+Ph4DR8+XB9//LFl+Zybm5uSkpLUuHFj5eTkyGw2q0aNGkpJSbF8LiEhQW5ubjp16pQkqX79+qpVq5Zq166t06dPa/Xq1Tpz5ox8fX315ptvKiYmRuPHj9eXX36pmjVr5rtPpUqVLNdzc3NTcnJygfuEh4crLi5O4eHhio+Pl9FolIeHR74XjAMAAAAAUNJERUWpXLlyatSokbWj3FesPCoGq1ev1oQJEywrbtLT02UymVStWjVt3LhRixYt0o8//qgff/xRn3/+uTZu3FjgGgMHDtTBgwcVExMjk8mk3r1753u5dHx8vDw9PfN9xs3NTfHx8ZafP/jgAwUHB6tJkyaWY+3bt1dYWJgkaefOnWrTpo0cHR1Vr149HT58WJK0ZcsW+fn56fTp05o5c6YkKTMzU2fOnJGnp6emTJmiJUuW6M0331ReXp6CgoI0e/bsfHn+fJ+b1/P09FRGRobOnz+v3Nxc7dy5U+3bt9dnn32mNWvW6F//+pf69eunYcOGWYqjhIQEeXh4/PXJAAAAAADgHsjNzdWJEyfk5eV1y50+pVmpXHl0v/Xp00enT59Wv379VL58eeXm5mr8+PE6e/asjEZjvkayVatWunz5coGtWQ4ODho9erSCg4O1YsUKhYaGKjg42DLu6empiRMn5vtM69at9d1330mSzpw5o8OHD2v27NmW8Zdfflk9e/ZURESEBg0aJKPRqI8++kiSFBQUpIkTJ8pkMsnb21u+vr4ym83av3+/Bg4cqOzsbL3xxhv53lkkSfv27dP58+c1adIky7FRo0YpMDBQo0aN0vPPPy8XFxfL+5KCg4P13nvvSZJ69uyphx56qNDv0Ww268SJE5oyZUqR3zkAAAAAAPdTbGysMjMzy9RfWbvJYC7qBTUlzJEjR/K9JNqWRUdH/+23s48YMUJDhgwpFb+827Zt0549e/KVZrg3iuN3D9bB3Nku5s52MXdlE/Nuu5g728Xc2a6yMnerVq1STEyMPvjgA8sfyCpNbte3sG3Nxo0fP16zZs1Sdna2taP8LRkZGVq8eLHeeecda0cBAAAAACCf3Nxc/fbbb2rSpEmpLI6KUvaeuJTx8PDQN998Y+0Yf1vFihW1ZMkSa8cAAAAAAKCAmJgY3bhxo1Ts+vkrWHkEAAAAAABwG8eOHVOFChVUv359a0exCsojAAAAAACAQty4cUPR0dF69NFHZWdXNmuUsvnUAAAAAAAAdyAmJkY5OTlq1qyZtaNYTZHvPDpw4IDefvttPfzww5Kkhg0b6vXXX9fo0aOVl5enGjVq6JNPPpHRaNT69eu1ePFi2dnZqX///urXr59ycnI0duxYXbx4Ufb29goJCVHt2rUVExNj+atajRo10uTJkyVJCxcuVFhYmAwGg0aMGKEnnnji3j09AAAAAADAbURFRcnFxUV169a1dhSruaOVR4899piWLFmiJUuWaMKECZo9e7aef/55LV++XHXq1NHq1at1/fp1ffHFF1q0aJGWLFmixYsXKyUlRRs2bJCLi4tWrFihoUOHKjQ0VJI0depUBQUFaeXKlcrIyNCuXbsUFxenTZs2afny5Zo/f75CQkKUl5d3T7+A4nD+/Hk1b95cgYGB+f5JSUnR2rVr9cQTTygwMFCDBw/Wq6++quTkZMtnFy1apGeffVYDBgzQgAEDdOjQIctY586dde3atVvec/78+Wrbtq1yc3NvOR4TE6ORI0dKkhYvXqy+ffvqueee07JlyyRJ6enpeuONNzRo0CC99tprSklJkfTHcrwxY8aoT58+Ba6ZlZUlf39/rV279pb3/PjjjzVgwAA999xz2rJliyTp0qVLCgwM1PPPP6+3337b8lfhUlNT9dprr+mtt97Kd42vv/5aTz/9tJ577jkdO3ZM8fHxevXVV5WTk3PLewIAAAAAcK9kZmbq5MmTatasWZndsib9xW1rBw4cUJcuXSRJnTp10r59+xQVFaVHH31UlSpVkpOTk1q0aKHIyEjt27dPXbt2lST5+voqMjJS2dnZunDhgmXJ181rHDhwQH5+fjIajXJ1dVWtWrUUGxtbTI96bz300EOWgu3mP1WqVJEk9ezZU0uWLNHSpUvVokULrVmzRpK0e/du7d27VytWrNCqVas0e/ZsTZ48WadPny7yfhs2bFCVKlUUERFxy/FJkyZp3LhxiouL09q1a7Vy5UqtWLFCX3/9tdLT07V48WI99thjWrFihbp166YFCxZI+qMA8vLyuuU1582bp8qVK99ybP/+/fr999+1atUqLVy4UNOmTZOkWxaNN/O1bNky3zV+//13bdy4UWvWrNGUKVMUHh4uDw8PPf744/ruu++K/E4AAAAAAChOv/32m/Ly8sr0ljXpDratSVJsbKyGDh2q1NRUjRgxQpmZmTIajZKkatWqKSkpScnJyXJ1dbV8xtXVtcBxOzs7GQwGJScny8XFxXLuzWtUqVLlltdo1KhRvjzR0dF//YnvgYSEBGVlZd0y18WLF3X58mXLWGxsrBo2bKjo6Gj9+OOPevvtt3XmzBnL+T179tScOXP0xhtvKDs7WydPnpSzs3O+a549e1aZmZnq3bu3li9frho1auQb/+2332Q0GpWamiqTyaRJkybp999/lyQZDAb98ssv2r59u0aOHKno6Gh5enpq0aJF6tWrl5588kmlpaUVeJ7z58/rl19+UZMmTXTx4sUCz1qxYkUNGzZM0dHRysvLU0ZGhk6cOKE9e/Zo0KBBio6O1kMPPaR169apefPmevHFFxUbG6u0tDTLtdauXauWLVvq999/l52dnbp27aro6Gh5e3vrH//4h3x9ff/GLOHPCvt9RcnH3Nku5s52MXdlE/Nuu5g728Xc2a7SPHfh4eFydHRURkZGqX3GO1FkeXHMbtMAACAASURBVFS3bl2NGDFCPXr0UFxcnF588cV8W8nMZvMtP3c3x+/2GoWtjLGIGFPw2AN+Ut1eUm6WdHBSwfHa/lLtrtKNVOnItPxjvtNve7ubq61ulSs6Olrff/+9Ll26pKtXr6pChQoKCQmRs7OzkpOT1bVrVxkMBsv5BoNBhw4dkpeXl4xGoxo1aqQKFSrku+amTZv07LPP6oUXXlBAQIDq1auncuXKWca3b9+uzp07F8izZ88ey0qeqVOnqnXr1qpUqZLy8vKUnp5uOf/8+fMFnufTTz/V1KlTtW7dOj3wwAO3nYNVq1apU6dOatq0qXJzcy0NbYUKFbRq1SrLZ3NycuTi4mL5OTs7W/b29poxY4Zyc3M1btw4NW7cWJJUs2ZNOTs7l+k9psUpOjq66P+OUCIxd7aLubNdzF3ZxLzbLubOdjF3tqu0zt21a9d0+fJl+fn56ZFHHrF2nHvuyJEjhY4VuW3N3d1dPXv2lMFg0IMPPqjq1asrNTVVWVlZkv5YdePm5iY3N7d87/JJTEy0HE9KSpL0R1lgNptVo0YNyzt2bneNm8dtwZkzZ/K972jixImWsZvb1jZs2KBBgwblG7uV2+2jNJvN2rhxo3r16qUqVarIx8dHu3btyndOYmKi3N3d8x07evSopk+frhkzZtzymrezbt06+fj4qHbt2rc9T5K2bdum1atX3/IZi7qP2WxWXl6eFi5cqJEjR+qDDz6wjLm7u+vSpUtF3h8AAAAAgOJw4sQJmUwmeXt7WzuK1RW58mj9+vVKSkrSa6+9pqSkJF2+fFl9+vTR5s2b9fTTT2vLli3y8/OTt7e3xo8fr7S0NNnb2ysyMlJBQUHKyMhQWFiY/Pz8tHPnTrVp00aOjo6qV6+eDh8+rFatWmnLli0KDAxU3bp19e2332rkyJG6evWqEhMT1aBBg7t/qtutFHJwuv14ucpFrjS6lZvvPCpK9+7dNWvWLEl/FCIxMTH5Gtro6OjbPnNkZKQuX75sedF0enq6Nm7cqG7duuU778+rmWJiYjR+/Hh9+eWXqlmzpiRZSr1KlSoVWdKFh4crLi5O4eHhio+Pl9FolIeHh5YsWaKMjAw99dRT6tevn3bv3q0vv/xSCxcuVKVKlSRJ5cuXV1ZWlpycnIq8T/Xq1VWvXj0ZDAa1atVKFy5cKPRcAAAAAADupaioKFWvXt3y/6PLsiLLo86dO+v999/X9u3blZOTo+DgYHl5eWnMmDFatWqVHnjgAT3zzDNydHTUe++9p9dee00Gg0HDhw9XpUqV1LNnT0VERGjQoEEyGo366KOPJElBQUGaOHGipcW7+T6b/v37a/DgwTIYDAoODi51bzOPiorSQw89JEnq3bu3pk+frnnz5snZ2VmJiYn65ptvNHfu3EI/v2HDBr3//vsKDAyUJF2/fl3+/v66du2aZXubm5ubEhISJEl5eXkKCgrS7Nmz5enpablO+/btFRYWpmHDhlkKwMJ89tlnln+fM2eOatWqJV9f33zvIEpPT9fHH3+sRYsWWV4ULv3xkvT/LRoL8/jjj2vlypXq1auXTp06le8/0ISEBHl4eBT6WQAAAAAAiktaWprOnDmjzp0751ucUVYVWR5VrFhRX375ZYHj3377bYFjAQEBCggIyHfM3t5eISEhBc5t0KCBli9fXuD4zW1ftubmtrU/GzVqlKQ/3lF04sQJy/Hg4GBJUocOHVS1alUNHDhQ5cqVk8Fg0KhRo/JtDxsyZIjs7e0lSb169dKOHTvy/Xn78uXLq2PHjtq+fbueeuopSVLbtm21aNEivfzyy9q3b5/Onz+vSZP+7z1Po0aNUmBgoEaNGqXnn39eLi4u+uSTTyRJb731luLj4y3P079/f/Xu3bvI59+0aZOuXr2qf/zjH5Zj06dP18iRIwsUjXl5eXr55ZeVlpamhIQEBQYGatiwYWrXrp1+/vlnDRgwQJIsW98yMzOVnJxsKd0AAAAAALiXTpw4IbPZXOb/ytpNBnNRL6IpYY4cOVLgT7zbqnv5UrH+/ftr1qxZpWJ53eLFi5Wdna0hQ4ZYO0qpUVpfaFcWMHe2i7mzXcxd2cS82y7mznYxd7arNM7d3LlzlZ2dnW+BRGl3u76ldO0Jg8XkyZNvueLL1sTHxys8PFwvvfSStaMAAAAAAMqAlJQU/fe//2XV0Z8UuW0NtsnLy0uzZ8+2doy/zcPD45ZbJAEAAAAAuBeOHTsmSfyVtT9h5REAAAAAAMD/FxUVJU9PT1WrVs3aUUoMyiMAAAAAAABJycnJunDhAlvW/gflEQAAAAAAgP5vyxrlUX6URwAAAAAAAPpjy1rdunVVpUoVa0cpUXhhdjE5f/68unTpolWrVsnHx8dy/LnnntPDDz8sSfr111/z/QI2adJEXl5e6ty5szw8PGRvby+TySQnJydNmzZN7u7uio+P14QJE5SZmamsrCw9/PDDmjx5soxG431/RgAAAAAASquEhAQlJCToqaeesnaUEofyqBjVrl1bGzZssJRH586dU1pammX83XffVadOnSw/R0dHW/59wYIFqlChgiRp7dq1mjVrlqZNm6ZZs2apT58+6tGjhyRp4sSJ2r17t7p06XI/HgkAAAAAgDIhKipKBoOBLWu3QHlUjLy9vRUREaG8vDzZ29tr48aNat++vbKysu76OmvWrJEkpaWlKSMjwzI2ZcqUYs0MAAAAAEBZZzabdezYMdWvX18VK1a0dpwSp1SWR1NiTxQ41rZKNXWrXlM3THmafjq6wPjjrjXU0dVdabk5+uzsyXxjExs0vaP7Ojo6ytvbWwcOHJCvr6+2b9+uESNGaPPmzXeVPywsTI888ogkaciQIRo2bJjWrl2r9u3bq3fv3qpTp85dXQ8AAAAAABTu0qVLSk5O1uOPP27tKCVSqSyPrCkgIEAbNmxQ9erV5e7urvLly1vGZs6cqW+++cbyc58+feTl5SXpj5LI3t5ecXFxatmypSZPnixJ8vHx0fbt27V37179/PPP6tu3rz799FN16NDh/j4YAAAAAACl1NGjR2VnZ6emTe9s8UhZUyrLo9utFCpnZ3/bcRcHxzteaXQr7dq105QpU1SjRg11794939idvPNo6dKlOnv2rGWZXFZWlpydneXv7y9/f381b95cGzdupDwCAAAAAKAY3Nyy9vDDD+dbAIL/Y2ftAKWN0WhU69attWbNGnXu3PmuPz9w4EAdPHhQMTExMplM6t27t2JjYy3j8fHx8vT0LM7IAAAAAACUWXFxcUpJSZG3t7e1o5RYpXLlkbUFBAToypUrqlSpUr7j/7ttzdXVVbNmzcp3joODg0aPHq3g4GCtWLFCoaGhCg4Otox7enpq4sSJ9zQ/AAAAAABlRVRUlBwcHCzvHkZBlEfFxNPTUx999JEkqWPHjurYsaMkqU2bNmrTps0tP3Nz29qOHTvyHe/QoYNlW1qzZs20dOnSe5QaAAAAAICyy2Qy6dixY2rUqJGcnJysHafEYtsaAAAAAAAok86ePav09HS2rBWB8ggAAAAAAJRJUVFRcnR0VOPGja0dpUSjPAIAAAAAAGVOTk6Ojh07Ji8vLxmNRmvHKdEojwAAAAAAQJlz9OhRZWZmqm3bttaOUuJRHgEAAAAAgDLFbDYrIiJCHh4eeuihh6wdp8SjPAIAAAAAAGXKmTNndOnSJfn6+spgMFg7TolHeQQAAAAAAMqUiIgIOTs7y8fHx9pRbALlEQAAAAAAKDNSUlL066+/qnXr1rwo+w5RHgEAAAAAgDJj//79kqR27dpZOYntoDwCAAAAAABlQk5Ojg4ePKhHHnlEVatWtXYcm0F5BAAAAAAAyoSoqChdv35dvr6+1o5iUyiPAAAAAABAqWc2m7V37165u7urXr161o5jUyiPAAAAAABAqXfu3DldunRJvr6+MhgM1o5jUyiPAAAAAABAqbd37145OzurefPm1o5icyiPAAAAAABAqZaamqoTJ06odevWMhqN1o5jcyiPAAAAAABAqbZ//35JUtu2ba2cxDZRHgEAAAAAgFIrNzdXBw8elJeXl1xdXa0dxyZRHgEAAAAAgFLr6NGjunbtmnx9fa0dxWZRHgEAAAAAgFLJbDYrIiJCbm5uql+/vrXj2CzKIwAAAAAAUCqdO3dOFy9elK+vrwwGg7Xj2CzKIwAAAAAAUCpFRETIyclJLVq0sHYUm0Z5BAAAAAAASp3U1FQdP35crVq1ktFotHYcm0Z5BAAAAAAASp0DBw5IEi/KLgaURwAAAAAAoFTJzc3VgQMH1LhxY7m6ulo7js2jPAIAAAAAAKXKsWPHdO3aNbVv397aUUoFyiMAAAAAAFBqmM1m7d27V25ubqpfv76145QKlEcAAAAAAKDU+O9//6sLFy7I19dXBoPB2nFKBcojAAAAAABQauzdu1dOTk5q3ry5taOUGpRHAAAAAACgVEhLS9OJEyfUqlUrlStXztpxSg3KIwAAAAAAUCrs379fZrNZ7dq1s3aUUoXyCAAAAAAA2Lzc3FwdPHhQjRo1UrVq1awdp1ShPAIAAAAAADbv2LFjysjIkK+vr7WjlDqURwAAAAAAwKaZzWZFRESoevXqatCggbXjlDqURwAAAAAAwKbFxcXp/Pnz8vX1lZ0dVUdx4xsFAAAAAAA2LSIiQuXKlVPLli2tHaVUojwCAAAAAAA2Ky0tTceOHVOrVq1Urlw5a8cplSiPAAAAAACAzTp48KBMJpPatWtn7SilFuURAAAAAACwSbm5udq/f78aNWqk6tWrWztOqUV5BAAAAAAAbNLx48eVkZEhX19fa0cp1SiPAAAAAACAzTGZTPr5559VvXp1Pfzww9aOU6pRHgEAAAAAAJtz/PhxXbp0SZ07d5adHfXGvcS3CwAAAAAAbEpubq42b94sDw8P+fj4WDtOqUd5BAAAAAAAbMrhw4d15coVBQQEsOroPuAbBgAAAAAANiM7O1vbt29X3bp11ahRI2vHKRMojwAAAAAAgM3Ys2eP0tPTFRAQIIPBYO04ZQLlEQAAAAAAsAnXr1/Xrl275OXlpbp161o7TplBeQQAAAAAAGxCeHi4srOz1b17d2tHKVMojwAAAAAAQImXmpqqiIgINW/eXB4eHtaOU6ZQHgEAAAAAgBJv69atMpvN8vf3t3aUMofyCAAAAAAAlGiJiYk6cuSI2rZtK1dXV2vHKXPuqDzKysqSv7+/1q5dq0uXLikwMFDPP/+83n77bWVnZ0uS1q9fr+eee079+vXT999/L0nKycnRe++9p0GDBmnw4MGKi4uTJMXExGjgwIEaOHCgJk2aZLnPwoUL1bdvX/Xr10+7du0q7mcFAAAAAAA2aMuWLTIajerUqZO1o5RJd1QezZs3T5UrV5YkzZ49W88//7yWL1+uOnXqaPXq1bp+/bq++OILLVq0SEuWLNHixYuVkpKiDRs2yMXFRStWrNDQoUMVGhoqSZo6daqCgoK0cuVKZWRkaNeuXYqLi9OmTZu0fPlyzZ8/XyEhIcrLy7t3Tw4AAAAAAEq8uLg4nThxQn5+fqpYsaK145RJRZZHp06dUmxsrDp27ChJOnDggLp06SJJ6tSpk/bt26eoqCg9+uijqlSpkpycnNSiRQtFRkZq37596tq1qyTJ19dXkZGRys7O1oULF9SsWbN81zhw4ID8/PxkNBrl6uqqWrVqKTY29h49NgAAAAAAKOnMZrN++uknVahQQX5+ftaOU2Y5FHXC9OnTNWHCBK1bt06SlJmZKaPRKEmqVq2akpKSlJycnG/Poaura4HjdnZ2MhgMSk5OlouLi+Xcm9eoUqXKLa/RqFGjApmio6P/4uOWLFlZWaXmWWBb+N2zXcyd7WLubBdzVzYx77aLubNdzJ3tupdzFxcXp99++02+vr46ffr0PbkHinbb8mjdunXy8fFR7dq1bzluNpv/9vG7vYYkeXl5FTpmS6Kjo0vNs8C28Ltnu5g728Xc2S7mrmxi3m0Xc2e7mDvbda/mzmQyaevWrfLw8FCfPn3k4FDk+hf8DUeOHCl07LbffHh4uOLi4hQeHq74+HgZjUaVL19eWVlZcnJyUkJCgtzc3OTm5qbk5GTL5xITE+Xj4yM3NzclJSWpcePGysnJkdlsVo0aNZSSkmI598/XOHPmTIHjAAAAAACg7Dl+/LguXryo/v37UxxZ2W3fefTZZ59pzZo1+te//qV+/fpp2LBh8vX11ebNmyX98bZzPz8/eXt76/jx40pLS9O1a9cUGRmpVq1aqX379goLC5Mk7dy5U23atJGjo6Pq1aunw4cP57tG27ZtFR4eruzsbCUkJCgxMVENGjS4x48PAAAAAABKmry8PG3ZskXu7u7y8fGxdpwy766ru5EjR2rMmDFatWqVHnjgAT3zzDNydHTUe++9p9dee00Gg0HDhw9XpUqV1LNnT0VERGjQoEEyGo366KOPJElBQUGaOHGiTCaTvL295evrK0nq37+/Bg8eLIPBoODgYNnZ3dEfgwMAAAAAAKXI4cOHdfnyZb344ot0AyXAHZdHI0eOtPz7t99+W2A8ICBAAQEB+Y7Z29srJCSkwLkNGjTQ8uXLCxwPDAxUYGDgnUYCAAAAAAClTHZ2trZt26Y6derwHqwSgvoOAAAAAACUGBEREUpPT1ePHj1kMBisHQeiPAIAAAAAACXE9evXFR4ersaNG6tu3brWjoP/j/IIAAAAAACUCOHh4bpx44a6d+9u7Sj4E8ojAAAAAABgdampqYqIiJCPj49q1qxp7Tj4E8ojAAAAAABgddu3b5fZbFbXrl2tHQX/g/IIAAAAAABYVVJSkg4dOqQ2bdrI1dXV2nHwPyiPAAAAAACAVW3evFmOjo7q3LmztaPgFiiPAAAAAACA1Zw/f14nTpyQn5+fKlasaO04uAXKIwAAAAAAYDVhYWGqUKGC/Pz8rB0FhaA8AgAAAAAAVhEbG6vY2Fh16tRJTk5O1o6DQlAeAQAAAACA+85sNissLEyVK1dW27ZtrR0Ht0F5BAAAAAAA7rvo6GidP39eXbp0kYODg7Xj4DYojwAAAAAAwH1lMpm0detWubq6qmXLltaOgyJQHgEAAAAAgPvq119/1aVLl+Tv7y97e3trx0ERKI8AAAAAAMB9c3PVUY0aNeTj42PtOLgDlEcAAAAAAOC+iYqKUmJiorp27So7O2oJW8AsAQAAAACA+8JkMmnbtm3y8PBQ06ZNrR0Hd4jyCAAAAAAA3BeRkZG6fPmyunXrxqojG8JMAQAAAACAey43N1fbtm2Tp6envLy8rB0Hd4HyCAAAAAAA3HOHDh1SSkqKunXrJoPBYO04uAuURwAAAAAA4J7KycnRzp07VadOHT388MPWjoO7RHkEAAAAAADuqQMHDigtLU3du3dn1ZENojwCAAAAAAD3zI0bNxQeHq769eurXr161o6Dv4DyCAAAAAAA3DP79u1TRkaGunXrZu0o+IsojwAAAAAAwD2RlZWlXbt2qWHDhqpTp4614+AvojwCAAAAAAD3xJ49e5SZmcmqIxtHeQQAAAAAAIrd9evXtXv3bjVp0kSenp7WjoO/gfIIAAAAAAAUu59//lnZ2dnq2rWrtaPgb6I8AgAAAAAAxSojI0MRERF69NFH5eHhYe04+JsojwAAAAAAQLEKDw9XTk4Oq45KCcojAAAAAABQbNLS0rR//341b95cNWrUsHYcFAPKIwAAAAAAUGx27twpk8kkf39/a0dBMaE8AgAAAAAAxeLq1as6ePCgWrVqJVdXV2vHQTGhPAIAAAAAAMVix44dkqTOnTtbOQmKE+URAAAAAAD425KTk3X48GG1adNGVapUsXYcFCPKIwAAAAAA8Ldt375dDg4O6tixo7WjoJhRHgEAAAAAgL8lMTFRR48eVbt27eTi4mLtOChmlEcAAAAAAOBv2bZtm4xGox5//HFrR8E9QHkEAAAAAAD+suTkZB07dky+vr6qWLGitePgHqA8AgAAAAAAf9nhw4fl5OQkPz8/a0fBPUJ5BAAAAAAA/pK4uDidO3dOfn5+Kl++vLXj4B6hPAIAAAAAAHfNZDLphx9+kLOzs9q3b2/tOLiHKI8AAAAAAMBd+/nnn3Xx4kV16NBBTk5O1o6De4jyCAAAAAAA3JWkpCRt27ZNTZs2Vb169awdB/cY5REAAAAAALhjJpNJq1evlqOjo55++mlrx8F9QHkEAAAAAADu2P79+3Xu3Dn16tVLlSpVsnYc3AeURwAAAAAA4I5cuXJFYWFhatiwoVq0aGHtOLhPKI8AAAAAAECRzGaz1q5dK4PBoD59+shgMFg7Eu4TyiMAAAAAAFCkI0eOKDY2VgEBAapSpYq14+A+ojwCAAAAAAC3lZaWpo0bN6pu3bpq06aNtePgPqM8AgAAAAAAhTKbzVq3bp1ycnLUt29f2dlRJZQ1zDgAAAAAACjU8ePH9dtvv6lr166qXr26tePACiiPAAAAAADALV2/fl0//vijatWqJT8/P2vHgZVQHgEAAAAAgFv697//rczMTLarlXHMPAAAAAAAKCAmJka//PKLOnbsqJo1a1o7DqyI8ggAAAAAAOSTlZWlH374QW5uburcubO148DKKI8AAAAAAEA+mzZtUlpamvr27SsHBwdrx4GVUR4BAAAAAACL06dP6+DBg2rfvr0efPBBa8dBCUB5BAAAAAAAJEnZ2dlavXq1XF1d1a1bN2vHQQlBeQQAAAAAACRJW7du1ZUrV/Tcc8/JaDRaOw5KCMojAAAAAACguLg47dmzR4899pjq169v7TgoQSiPAAAAAAAo43Jzc/X999/LxcVFPXv2tHYclDCURwAAAAAAlHHh4eFKTEzUs88+KycnJ2vHQQlDeQQAAAAAQBkWHx+vHTt2yMfHR40bN7Z2HJRAlEcAAAAAAJRRJpNJq1evlrOzs3r37m3tOCihKI8AAAAAACijdu/erfPnz+upp55ShQoVrB0HJZRDUSdkZmZq7Nixunz5sm7cuKFhw4apcePGGj16tPLy8lSjRg198sknMhqNWr9+vRYvXiw7Ozv1799f/fr1U05OjsaOHauLFy/K3t5eISEhql27tmJiYhQcHCxJatSokSZPnixJWrhwocLCwmQwGDRixAg98cQT9/QLAAAAAACgLEpKStLWrVv1yCOPqFmzZtaOgxKsyJVHO3fuVNOmTbV06VJ99tln+uijjzR79mw9//zzWr58uerUqaPVq1fr+vXr+uKLL7Ro0SItWbJEixcvVkpKijZs2CAXFxetWLFCQ4cOVWhoqCRp6tSpCgoK0sqVK5WRkaFdu3YpLi5OmzZt0vLlyzV//nyFhIQoLy/vnn8JAAAAAACUJSaTSWvWrJGjo6OeeeYZGQwGa0dCCVZkedSzZ08NGTJEknTp0qX/x97dh0dV3/n/f81MZnI3mcwkIXfchZCCgNwUUATFSkGLqBUVKCJ0bdn9dbe22+3qtl5sL1e7V6tV6be22127drVuFWHBG7yhoFRQEMQCYkUSFBGBhNzfTJLJJHNzfn/QTDMkCMjNmTN5Pq7rXHPmfM6ZvM98Pswhr5xzRgUFBdqxY4dmzpwpSZoxY4a2b9+u9957T2PHjlVWVpbS0tI0ceJE7d69W9u3b9fVV18tSZo2bZp2796trq4uVVZWxpLN7tfYsWOHpk+fLpfLpZycHA0cOFAHDhw4X/sOAAAAAEC/9Pbbb+vQoUO6/vrr5fF4zC4HCe6Ul611W7hwoaqrq/Xoo4/qG9/4hlwulyQpNzdXdXV1qq+vV05OTmz9nJycXsvtdrtsNpvq6+vjBmf3a3i93j5fY+TIkXG1lJeXf769TTDBYDBp9gXWwtizLvrOuug766Lv+if63broO+ui7y4cv9+v5557TkVFRUpPTz/r952+S36nHR6tXLlS5eXl+pd/+RcZhhFb3nO+pzNZfqavMWrUqFOVawnl5eVJsy+wFsaeddF31kXfWRd91z/R79ZF31kXfXdhGIah//mf/1FmZqaWLl0qr9d71q9J3yWHXbt2nbTtlJet7d27V8eOHZN0PLSJRCLKzMxUMBiUJNXU1Cg/P1/5+fmqr6+PbVdbWxtbXldXJ0kKhUIyDEMDBgxQc3NzbN2TvUb3cgAAAAAAcPb+9Kc/6cCBA7r22mvPSXCE/uGU4dHOnTv1+OOPS5Lq6+sVCAQ0bdo0bdiwQZL06quvavr06Ro/frzef/99+f1+tbe3a/fu3Zo8ebIuv/xyrV+/XtLxm29PmTJFTqdTpaWl2rlzZ9xrXHbZZdq8ebO6urpUU1Oj2tpalZWVna99BwAAAACg32hpadErr7yi0tJSXXrppWaXAws55WVrCxcu1L/+679q0aJFCgaDuueee3TxxRfrhz/8oVatWqXi4mLNnTtXTqdTd955p5YuXSqbzaY77rhDWVlZmjNnjrZt26Zbb71VLpdLDzzwgCRp2bJluueeexSNRjV+/HhNmzZNkrRgwQItXrxYNptN9957r+z2U+ZbAAAAAADgMxiGoeeff17RaFS33HILv2vjjJwyPEpLS9Py5ct7LX/iiSd6LZs9e7Zmz54dt8zhcOj+++/vtW5ZWZlWrFjRa/mSJUu0ZMmSU5UFAAAAAABO0549e1RRUaHrr79eubm5ZpcDiyFqBAAAAAAgibW1tenFF1/UkCFDYlf9AGeC8AgAAAAAgCS2du1adXV1ad68eVyuhs+FUQMAAAAAQJLau3ev3n//fc2aNYtvM8fnRngEAAAAAEASCgQCeuGFF1RcXKwrr7zS7HJgYYRHAAAAAAAkoZdeekmBQEDz5s2Tw+EwuxxYGOERAAAAAABJpqKiQu+++66uuuoqFRcXm10OLI7wCAAAAACAJBIMBvX8888rPz9fX/7yl80uB0mA8AgAAAAAgCSybt06+f1+zZ8/XykpKWaXgyRAeAQAAAAAQJI4S58MdwAAIABJREFUcOCA3nnnHU2fPl2DBw82uxwkCcIjAAAAAACSQFdXl5599lnl5eXp6quvNrscJBHCIwAAAAAAksCGDRvU1NSkW265RU6n0+xykEQIjwAAAAAAsLhDhw5p27Ztmjp1qoYNG2Z2OUgyhEcAAAAAAFhYOBzWmjVrlJ2drdmzZ5tdDpIQ4REAAAAAABa2ceNG1dfXa968eUpNTTW7HCQhwiMAAAAAACzq8OHDeuONN3TJJZeorKzM7HKQpAiPAAAAAACwoGAwqGeeeUZer1fXXXed2eUgiREeAQAAAABgQWvXrlVzc7MWLlyotLQ0s8tBEiM8AgAAAADAYnbv3q13331Xs2bN0tChQ80uB0mO8AgAAAAAAAtpbGzU2rVrVVJSohkzZphdDvoBwiMAAAAAACwiEonomWeekc1m08KFC2W382s9zj9GGQAAAAAAFvHHP/5RR44c0c033yyv12t2OegnCI8AAAAAALCAgwcPatOmTZo8ebLGjRtndjnoRwiPAAAAAABIcIFAQKtWrVJubq5uuOEGs8tBP0N4BAAAAABAAjMMQ88995xaW1u1cOFCpaamml0S+hnCIwAAAAAAEtiuXbu0d+9efeUrX9GgQYPMLgf9EOERAAAAAAAJqq6uTmvXrtXw4cM1ffp0s8tBP0V4BAAAAABAAgqHw3rmmWfkdDq1YMEC2e38Cg9zMPIAAAAAAEhAr776qqqqqjRv3jxlZ2ebXQ76McIjAAAAAAASzIEDB/Tmm29qypQpGj16tNnloJ8jPAIAAAAAIIG0t7dr1apVys/P13XXXWd2OQDhEQAAAAAAicIwDK1Zs0aBQEALFy6Uy+UyuySA8AgAAAAAgESxY8cOlZeX69prr1VxcbHZ5QCSCI8AAAAAAEgINTU1evnllzVixAhdfvnlZpcDxBAeAQAAAABgsnA4rGeeeUZpaWlasGCBbDab2SUBMYRHAAAAAACYbN26daqurtb8+fPldrvNLgeIQ3gEAAAAAICJKioqtG3bNl1++eUaOXKk2eUAvRAeAQAAAABgkpaWFq1evVqFhYW69tprzS4H6BPhEQAAAAAAJohEIlqxYoVCoZAWLVqklJQUs0sC+kR4BAAAAACACdavX69PP/1Ut9xyi/Lz880uBzgpwiMAAAAAAC6wvXv3asuWLZo6darGjx9vdjnAZyI8AgAAAADgAqqvr9fq1as1aNAgXXfddWaXA5wS4REAAAAAABdIKBTS008/Lbvdrttuu437HMESCI8AAAAAALhA1q5dq2PHjmnhwoXy+XxmlwOcFsIjAAAAAAAugJ07d2rnzp368pe/rJEjR5pdDnDaCI8AAAAAADjPqqqq9MILL2j48OGaNWuW2eUAZ4TwCAAAAACA8ygYDOrpp59WRkaGbr31Vtnt/CoOa2HEAgAAAABwnhiGodWrV6uxsVGLFi2S2+02uyTgjBEeAQAAAABwnmzdulUffPCB5syZo5KSErPLAT4XwiMAAAAAAM6DQ4cOad26dRozZoyuuOIKs8sBPjfCIwAAAAAAzrG2tjatWLFCPp9P8+fPl81mM7sk4HMjPAIAAAAA4ByKRqNauXKlAoGAFi9erLS0NLNLAs4K4REAAAAAAOfQxo0bdeDAAc2dO1fFxcVmlwOcNcIjAAAAAADOkf379+v111/X5MmTNXnyZLPLAc4JwiMAAAAAAM6BpqYmrVy5UoWFhbrxxhvNLgc4ZwiPAAAAAAA4S+FwWE8//bSi0agWL14sp9NpdknAOUN4BAAAAADAWXrllVd09OhRzZ8/X3l5eWaXA5xThEcAAAAAAJyFPXv2aPv27briiit08cUXm10OcM6lmF0AAAAAAABWtWPHDq1du1ZDhw7Vtddea3Y5wHlBeAQAAAAAwBmKRqN65ZVX9NZbb2nEiBFatGiRHA6H2WUB5wXhEQAAAAAAZyAYDGrFihX68MMPdcUVV2jOnDmy27krDJIX4REAAAAAAKepoaFBTz75pOrr63XTTTdpypQpZpcEnHeERwAAAAAAnIaDBw/qqaeekmEYWrp0qYYPH252ScAFQXgEAAAAAMAp/OlPf9ILL7wgn8+n22+/XXl5eWaXBFwwhEcAAAAAAJxENBrVH/7wB23ZskVlZWW67bbblJ6ebnZZwAVFeAQAAAAAQB+CwaBWrlypiooKTZ06VTfccAM3xka/dFrh0YMPPqhdu3YpHA7rW9/6lsaOHasf/OAHikQiGjBggB566CG5XC69+OKLevLJJ2W327VgwQLNnz9foVBId999t6qqquRwOHT//fdr8ODBqqio0L333itJGjlypO677z5J0m9/+1utX79eNptN3/nOd/SlL33pvO08AAAAAAB9aWxs1O9+9zvV1dVp7ty5uuyyy8wuCTDNKcOjt99+Wx999JFWrVqlpqYm3XTTTZo6daoWLVqka6+9Vj//+c+1Zs0azZ07V7/+9a+1Zs0aOZ1OzZs3T1dffbU2bdokj8ej5cuXa+vWrVq+fLl+8Ytf6Cc/+YmWLVumcePG6c4779Qbb7yh0tJSrVu3TitXrlRbW5sWLVqkK664Qg6H40K8FwAAAAAA6NChQ/r973+vaDSqpUuXqqyszOySAFOd8ny7Sy65RI888ogkyePxqKOjQzt27NDMmTMlSTNmzND27dv13nvvaezYscrKylJaWpomTpyo3bt3a/v27br66qslSdOmTdPu3bvV1dWlyspKjRs3Lu41duzYoenTp8vlciknJ0cDBw7UgQMHzte+AwAAAAAQZ+fOnXrssceUnp6uO+64g+AI0GmceeRwOJSRkSFJWrNmja688kpt3bpVLpdLkpSbm6u6ujrV19crJycntl1OTk6v5Xa7XTabTfX19fJ4PLF1u1/D6/X2+RojR46Mq6m8vPwsdjlxBIPBpNkXWAtjz7roO+ui76yLvuuf6Hfrou+sy+y+i0ajeuedd/Tee+9p4MCBmjlzpurq6lRXV2daTVZhdt/h/DvtG2Zv3LhRa9as0eOPP65rrrkmttwwjD7XP5PlZ/oao0aNOlW5llBeXp40+wJrYexZF31nXfSdddF3/RP9bl30nXWZ2XednZ1auXKl9u/fr6uuuko33HADt085A/y7Sw67du06adtp3SZ+y5YtevTRR/XYY48pKytLGRkZCgaDkqSamhrl5+crPz9f9fX1sW1qa2tjy7uT2lAoJMMwNGDAADU3N8fWPdlrdC8HAAAAAOB8qK6u1q9//WtVVFToq1/9qubOnUtwBJzglOFRa2urHnzwQf3mN7+R1+uVdPzeRRs2bJAkvfrqq5o+fbrGjx+v999/X36/X+3t7dq9e7cmT56syy+/XOvXr5ckbdq0SVOmTJHT6VRpaal27twZ9xqXXXaZNm/erK6uLtXU1Ki2tpbrSwEAAAAA55xhGHr77bf1H//xH+ro6NDSpUs1bdo0s8sCEtIpL1tbt26dmpqa9E//9E+xZQ888IB+9KMfadWqVSouLtbcuXPldDp15513aunSpbLZbLrjjjuUlZWlOXPmaNu2bbr11lvlcrn0wAMPSJKWLVume+65R9FoVOPHj4/9I12wYIEWL14sm82me++9V3b7aZ0cBQAAAADAaQkEAnr22Wf1wQcfaMSIEVqwYIHcbrfZZQEJ65Th0de+9jV97Wtf67X8iSee6LVs9uzZmj17dtwyh8Oh+++/v9e6ZWVlWrFiRa/lS5Ys0ZIlS05VFgAAAAAAZ+zQoUN65pln1Nraqjlz5uiKK67gpAXgFE77htkAAAAAAFhVNBrV5s2b9dprr8nn8+nb3/62Bg0aZHZZgCUQHgEAAAAAkprf79fKlSt18OBBjR8/XjfddJPS0tLMLguwDMIjAAAAAEDSqqio0OrVq9XV1aV58+Zp0qRJstlsZpcFWArhEQAAAAAg6YTDYa1fv15bt25VYWGhFi1apPz8fLPLAiyJ8AgAAAAAkFTq6+u1YsUKVVVVaerUqbruuuuUksKvv8Dnxb8eAAAAAEDS2L17t9auXSu73a4lS5ZozJgxZpcEWB7hEQAAAADA8jo7O7V27Vrt3r1bJSUlWrhwobxer9llAUmB8AgAAAAAYGlHjx7VypUr1dDQoJkzZ2rmzJmy2+1mlwUkDcIjAAAAAIAlhcNh/fGPf9TmzZuVlZWlv/u7v1NpaanZZQFJh/AIAAAAAGA5R48e1erVq1VTU6OJEyfq+uuvV0ZGhtllAUmJ8AgAAAAAYBknnm10++2366KLLjK7LCCpER4BAAAAACzh6NGj+r//+z/V1tZq0qRJuv7665Wenm52WUDSIzwCAAAAACS0cDisjRs36o033uBsI8AEhEcAAAAAgITV82yjyZMn67rrruNsI+ACIzwCAAAAACScnmcbeTwefeMb39DIkSPNLgvolwiPAAAAAAAJ5ciRI1q9enXsbKPrr79eaWlpZpcF9FuERwAAAACAhBAOh/Xaa6/pzTff5GwjIIEQHgEAAAAATFdbW6s//OEPnG0EJCDCIwAAAACAafx+vzZu3KgtW7YoPz9f3/zmNzVixAizywLQA+ERAAAAAOCC6+jo0BtvvKG33npLkUhEo0eP1pIlSzjbCEhAhEcAAAAAgAsmHA5r27Zt2rRpkzo6OjRu3Dhdc801qqurIzgCEhThEQAAAADgvItGo9q9e7dee+01tbS0aMSIEZo9e7aKi4slSXV1dSZXCOBkCI8AAAAAAOeNYRjat2+fNmzYoNraWg0aNEgLFizQ8OHDzS4NwGkiPAIAAAAAnBcHDx7U+vXrdfjwYeXl5Wnx4sUaM2aMbDab2aUBOAOERwAAAACAc+rYsWPasGGDKioq5PF4dPPNN2vy5Mmy2+1mlwbgcyA8AgAAAACcE42NjXrttde0Z88epaWlafbs2Zo2bZpcLpfZpQE4C4RHAAAAAICz0tbWpk2bNuntt9+WzWbT9OnTddVVVykjI8Ps0gCcA4RHAAAAAIDPJRAI6M0339Rbb72lcDisyZMna9asWcrOzja7NADnEOERAAAAAOCMBINBbd26VVu2bFFXV5fGjx+vWbNmKS8vz+zSAJwHhEcAAAAAgNPS2dmpbdu26c0331RHR4cuvvhizZo1S4WFhWaXBuA8IjwCAAAAAHymUCikt99+W5s3b1Z7e7suuugiXXPNNSouLja7NAAXAOERAAAAAKBP4XBY77zzjjZt2qTW1laVlZXpmmuu0ZAhQ8wuDcAFRHgEAAAAAIgTiUS0a9cuvf7662publZJSYluvfVWlZaWml0aABMQHgEAAAAAJEnRaFR79uzRxo0b1djYqMGDB+uWW25RWVmZbDab2eUBMAnhEQAAAAD0c4ZhaO/evXr11VdVV1enoqIi/c3f/I0uuugiQiMAhEcAAAAA0J+Fw2E9//zz2rVrl/Lz87V48WKNHj1adrvd7NIAJAjCIwAAAADop5qbm/XUU0/p6NGj+vKXv6xZs2YRGgHohfAIAAAAAPqhjz/+WCtWrFA4HNbXv/51jR492uySACQowiMAAAAA6EcMw9Bbb72lV155RXl5eVqyZIny8/PNLgtAAiM8AgAAAIB+oqurS88995z27NmjMWPGaP78+UpLSzO7LAAJjvAIAAAAAPqBxsZG/f73v1d1dbWuueYaXXXVVdzfCMBpITwCAAAAgCT34Ycf6plnnpEk3X777Ro5cqTJFQGwEsIjAAAAAEhShmHojTfe0IYNG1RQUKAlS5YoNzfX7LIAWAzhEQAAAAAkoc7OTq1evVp79+7VuHHjNG/ePLlcLrPLAmBBhEcAAAAAkGTq6+v1v//7v6qrq9OcOXM0ffp02Ww2s8sCYFGERwAAAACQRMrLy7Vq1SrZ7XYtXbpUZWVlZpcEwOIIjwAAAAAgCUSjUb3++uvauHGjiouLtWTJEvl8PrPLApAECI8AAAAAwEKi0ahaW1vV3NwcN1VWVurw4cOaOHGibrrpJjmdTrNLBZAkCI8AAAAAIIEEg8FewVDPye/3KxqNxm2TlpYmr9erG2+8UZdddhn3NwJwThEeAQAAAICJWltb9f777+vPf/6zjh07ps7Ozrh2u92u7Oxseb1eDRs2TF6vN27Kzs5WWlqaSdUD6A8IjwAAAADgAgsEArHA6ODBgzIMQwUFBZo4caJ8Pl9cOOR2u2W3280uGUA/RngEAAAAABdAMBjUBx98oPfee08HDhxQNBpVXl6eZsyYofHjx6ugoMDsEgGgT4RHAAAAAHCedHV1qby8XO+9957279+vSCQir9er6dOna/z48SoqKuL+RAASHuERAAAAAJxD4XBYFRUV+vOf/6zy8nKFQiF5PB5NnTpV48aN0+DBgwmMAFgK4REAAAAAnKXW1lZ98sknKi8v1759+9TZ2anMzExNmjRJ48aNU0lJCfctAmBZhEcAAAAAcAYMw1BDQ4MOHTqkTz75RJ988okaGxslSenp6Ro7dqwmTJig0tJSAiMASYHwCAAAAAA+QzQaVVVVlQ4dOhSb2traJEkZGRkqKSnRZZddppKSEg0cOFAOh8PkigHg3CI8AgAAAIAeQqGQjhw5Ejuz6PDhw+rs7JQk+Xw+feELX1BJSYmGDRumvLw8zi4CkPQIjwAAAAD0W52dnaqpqVF1dbWqq6t19OhRVVZWKhKJyGazqaCgQF/84hdjYVF2drbZJQPABUd4BAAAACDpRaNRNTQ0qLq6WseOHYuFRd33KpKk1NRUFRYW6oorrlBJSYmGDh2qjIwME6sGgMRAeAQAAAAgqbS1tcXCoe6wqLa2VqFQSJJks9mUl5engQMHavLkySosLFRhYaG8Xi+XoAFAHwiPAAAAAFhSNBpVfX29qqqqVFVVpWPHjunAgQMyDCO2jtvtVmFhoaZMmaLCwkIVFRUpPz9fTqfTxMoBwFoIjwAAAAAkvM7OzthZRN1hUU1NTexsIofDoYKCAg0ePFjjxo2LnU3kdrtNrhwArI/wCAAAAEDCMAxDra2tsZCo+7GhoSF2RlF6erqKioo0ZcoUFRcXq6ioSAMGDFBKSorKy8s1atQok/cCAJIL4REAAAAAU0SjUTU2NqqysjIuKGpra4ut4/P5VFxcrAkTJqi4uFjFxcXKzs6WzWYzsXIA6F8IjwAAAACcd+FwWDU1NbFLzqqqqlRdXa3Ozk5Jxy87y8/P18iRI2NnExUVFSk9Pd3kygEApxUeffjhh/r2t7+t22+/XYsXL9axY8f0gx/8QJFIRAMGDNBDDz0kl8ulF198UU8++aTsdrsWLFig+fPnKxQK6e6771ZVVZUcDofuv/9+DR48WBUVFbr33nslSSNHjtR9990nSfrtb3+r9evXy2az6Tvf+Y6+9KUvnbedBwAAAHDuBYPBuJtYd9+fKBqNSpJSU1NVVFSkSZMmxc4mys/PV0oKf9sGgER0yk/nQCCgf//3f9fUqVNjy375y19q0aJFuvbaa/Xzn/9ca9as0dy5c/XrX/9aa9askdPp1Lx583T11Vdr06ZN8ng8Wr58ubZu3arly5frF7/4hX7yk59o2bJlGjdunO6880698cYbKi0t1bp167Ry5Uq1tbVp0aJFuuKKK+RwOM7rmwAAAADgzBmGoebm5lhAVF1draqqKjU2NsbWcbvdKi4ujp1RVFxcrJycHNntdhMrBwCciVOGRy6XS4899pgee+yx2LIdO3bEzhSaMWOGHn/8cQ0bNkxjx45VVlaWJGnixInavXu3tm/frrlz50qSpk2bpmXLlqmrq0uVlZUaN25c7DW2b9+uuro6TZ8+XS6XSzk5ORo4cKAOHDigkSNHnvMdBwAAAHD6wuGwqqurYwHRsWPHVF1drY6ODkmSzWZTbm6uiouLNXny5FhQ5PF4TK4cAHC2ThkepaSk9Dp9tKOjQy6XS5KUm5ururo61dfXKycnJ7ZOTk5Or+V2u102m0319fVxB5Hu1/B6vX2+xonhUXl5+efY1cQTDAaTZl9gLYw966LvrIu+sy76rn/q7/0eCATU0NAQNzU3N8e+7SwlJUW5ubkqLCxUXl6ecnJylJOTE/sdQTp+VlJlZaUqKysvaO39ve+sjL6zLvou+Z31RcXdB5CzWX6mr5EsX73J14jCLIw966LvrIu+sy76rn9K9n6PRCJqbm5WU1OTGhoa1NTUpMbGRjU2NqqpqUnt7e2xdbOzs1VSUhK7gXVRUVFCX3aW7H2XzOg766LvksOuXbtO2va5wqOMjAwFg0GlpaWppqZG+fn5ys/PV319fWyd2tpaTZgwQfn5+aqrq9NFF12kUCgkwzA0YMAANTc3x9bt+RqffPJJr+UAAAAATp9hGGpra4sLhLrnGxsb1dLSEveHWrvdLp/PJ5/PpzFjxig/P1/FxcUqLCxURkaGiXsCAEgEnys8mjZtmjZs2KAbb7xRr776qqZPn67x48frRz/6kfx+vxwOh3bv3q1ly5apra1N69ev1/Tp07Vp0yZNmTJFTqdTpaWl2rlzpyZPnqxXX31VS5YsUUlJiZ544gl997vfVVNTk2pra1VWVnau9xkAAACwrGg0qkAgoJaWFrW0tMjv9/eab25uVigUitsuKytLPp9Pw4YNk8/ni11qlpOTI4/Hk7BnEgEAzHfK8Gjv3r362c9+psrKSqWkpGjDhg16+OGHdffdd2vVqlUqLi7W3Llz5XQ6deedd2rp0qWy2Wy64447lJWVpTlz5mjbtm269dZb5XK59MADD0iSli1bpnvuuUfRaFTjx4/XtGnTJEkLFizQ4sWLZbPZdO+993IQAwAAQL8RjUbl9/vV3NzcZyjU0tKi1tZWRSKRuO1sNps8Ho+ys7NVUFCgkSNHxoVDPp9PTqfTpL0CAFjdKcOjiy++WL///e97LX/iiSd6LZs9e7Zmz54dt8zhcOj+++/vtW5ZWZlWrFjRa/mSJUu0ZMmSU5UFAAAAWE5XV1fsXkPNzc2xqfv5iZeTSZLT6VR2drY8Ho+GDRsWm8/Ozo7Nu91u/ugKADhvzvqG2QAAAACOB0NtbW1qbW1Va2trLBDqGRQFAoG4bWw2m7Kzs2OXk3m9Xvl8vrhgKD09XTabzaS9AgCA8AgAAAA4qe5AqDsU6vnY1tYmv9+vtrY2tbe3q7Ozs9f2qampys7Oltfr1eDBg2PhkNfrldfr5V5DAABLIDwCAABAv2MYRuym0933F+q+bKylpUWVlZWS1GcgJB3/9mG32y23263BgwcrKysr9jwrK0tZWVnyer2cNQQASAqERwAAAEgq3d9G1vPG0z2Doe4pHA7HbWe322P3EsrNzdWwYcNiYVDPYCgzM1MpKfw3GgDQf3DUAwAAQMKLRqNqb2+Pu2TsxMvJek4n3nS6ZzA0cOBAjRkzRh6PR16vN3Z/oZ43nS4vL9eoUaPM2FUAABIO4REAAAAumGg0qmAwqGAwqEAgoGAwqI6OjtjU/TwQCCgQCMSCoUAg0CsQkqSUlJTYWUHdwVD3mULdodCJwRAAADgzhEcAAAA4bYZhKBQKxYU93SHQqQKhjo6Ok95DqJvNZlN6errS09OVmZmp3NxcDR06NBYIdV821v08NTWVewoBAHCeER4BAAAkuWg0qlAopK6uLnV2dqqrqys2HwqF1NnZGbe8e+oZ+nTPB4NBRSKRz/x5qampSktLU3p6utLS0uT1elVUVBQLhbrbuuczMjJiy1wuF2EQAAAJhvAIAADABNFoVOFwOG6KRCIKhUKKRCKxZd2hT1dXl0KhUOz56Tx2B0OhUOi067LZbHK5XHK5XEpNTVV6eroyMjKUk5MTC3xOFQJxeRgAAMmF8AgAAPR74XBY7e3tcVP3fXYqKyv18ccfKxKJKBKJKBqN9nr8rGUnhkHdU1/37zldNptNTqczNnWHPU6nUxkZGfJ6vbG21NRUpaamxtbpGQz1fOz5Gpz5AwAAeiI8AgAAlmYYRp/hTPcUCARiQVD3N3GdGBQFg8E+X9tms6mrqyt2s2WHwyGHwyG73R573nO++3lKSkps3ul0yuFwKCUlJW7qa1lfU3c41PMxJSWFgAcAAFwwhEcAAOC0dJ9N0315VXdg09f8ict6Pp64TV9t3fM9L+3qPoPnxMdwOHza+2C325WZmanMzEy53W55vV653e7Y8+627udpaWnav38/X9kOAAD6NcIjAAD6oUgkErs061RTR0fHWV9mdTLdZ9/0fOx5Ro7D4YhdetV9Bs+ZPqanp8vtdisjI0Pp6emcsQMAAHCGCI8AAEgS0WhUgUBAfr9fra2tcdOJgVAgEOjzNVJSUuR2u+V2u+XxeFRcXKyMjIy4UKd7/sTgp+flWicuO1kwRJADAACQ+AiPAABIcKFQKC4I6isc6g6I+jo7KDU1VVlZWXK73crPz1dpaWksIDpxSk1NJdABAABAHMIjAABMEo1GY8FPS0uL/H5/bOp+3traqo6Ojl7b2mw2ud1uZWVlKSsrS8XFxbF5j8cTm3e73XK5XCbsHQAAAJIF4REAAOeYYRgKBAKxs4T8fr8qKiq0f/9+tbS0xMKivs4UstlssfBnwIABGj58eK9AyOPxKCMjQ3a73aQ9BAAAQH9CeAQAwGkyDEMdHR1xoVDP+Z7LIpFI3LbBYFA5OTnyeDzyeDwqLCyMzXdP2dnZyszMJBQCAABAQiE8AgDgJBobG7Vv3z7t379fDQ0N8vv9fX4tfFpaWiwAGjZsWGy++ywhj8ejyspKjR071oS9AAAAAM4O4REAAH9hGIYqKyu1b98+7du3T9XV1ZKk/Px8DRkypM9QKCsr67TuKVRTU3O+ywcAAADOC8IjAEC/Fg6H9fHHH2vfvn0qLy+X3++XzWZTSUmJ5syZo9GjRysvL8/sMgEAAADTEB4BAPqdQCCgiooK7du3Tx999JE6Ozvlcrk0YsQIjR49WiNHjlRmZqbZZQIAAAAJgfAIANAvdN+/aN++ffrkk09kGIb7awFiAAAbV0lEQVSysrI0fvx4jR49WmVlZUpJ4bAIAAAAnIj/JQMAklpNTY2ef/55HTp0SNLx+xddddVVGj16tAYOHMg3mwEAAACnQHgEAEhK4XBYmzdv1qZNm5Samqo5c+ZozJgxys3NNbs0AAAAwFIIjwAASefTTz/Vs88+q9raWk2YMEHXX3+93G632WUBAAAAlkR4BABIGp2dndqwYYO2b98uj8ejb3zjGxo5cqTZZQEAAACWRngEAEgK+/fv1/PPP6+WlhZNnTpVX/nKV5Sammp2WQAAAIDlER4BACytra1NL7/8svbs2aP8/Hz9/d//vYYOHWp2WQAAAEDSIDwCAFiSYRh699139fLLL6uzs1OzZs3SVVddpZQUDm0AAADAucT/sAEAltPU1KTnn39eH374oYYMGaJbbrlFBQUFZpcFAAAAJCXCIwCAZUSjUW3fvl0bNmyQJH31q1/VZZddJrvdbnJlAAAAQPIiPAIAWEJNTY2effZZHT58WCNGjNBNN90kn89ndlkAAABA0iM8AgAkhK6uLrW1tam1tTXusa2tTX6/X/v371dqaqq+9rWvacKECbLZbGaXDAAAAPQLhEcAgPMmHA7L7/ertbU1LgzqKyDq6urq8zUyMjLkdrv1xS9+UbNnz5bb7b7AewEAAAD0b4RHAIAzYhiGOjo6YoHQiZPf748FQx0dHX2+Rncg5Ha7NXjwYGVlZcWe95x3u91yOBwXeA8BAAAA9ER4BACI09nZqebmZjU1NcWm5uZmNTc3x4KhcDjca7uUlBRlZWUpKytL+fn5Gj58eOx5dyDU/UggBAAAAFgH4REA9DPBYFCNjY1xAVHP+UAgELe+w+GQ1+uV1+vVsGHD4gKh7snj8Sg1NZX7EAEAAABJiPAIAJKMYRhqbW1VfX29GhoaYo8NDQ1qampSMBiMW9/pdMrr9crn82ngwIHKycmJPff5fHK73bLb7SbtDQAAAACzER4BgAWdLCDqfgyFQrF1HQ6HfD6fcnNzVVJSIp/PJ6/XGwuJMjMzOWMIAAAAwEkRHgFAggqHw2publZjY6OamprU2NioDz/8UBs2bDhpQJSXl6eysjLl5OQoLy9PeXl58nq9nDkEAAAA4HMjPAIAk0SjUfn9fjU2NsYCou6QqKmpSX6/X4ZhxNZ3OBxKSUnRsGHDVFZWptzcXOXm5hIQAQAAADivCI8A4DyKRqNqbm5WTU2Namtr1dDQEAuLWlpaFIlEYuvabDZ5PB7l5ORo+PDhysnJid13KCcnRx6PR/v379eoUaNM3CMAAAAA/Q3hEQCcA4ZhyO/3q6amJjZVV1ertrZWXV1dsfUyMzPl8/k0aNAgjRs3LhYMdd+HKCWFj2UAAAAAiYXfUgDgDBiGofb2dlVXV/cKiXp+i5nb7VZBQYEmT56sgoKC2JSenm5i9QAAAABw5giPAKAP4XBYjY2Nqq+vV11dXeyxrq5O7e3tsfXS09NVWFioCRMmxIVEmZmZJlYPAAAAAOcO4RGAfqv7htXdoVB9fX1sampqirtZtdvtVl5enkaPHq2CggIVFhaqoKBAbrebr7kHAAAAkNQIj0xUePD/SU2++IXF06WS66VwUHrn33pvNHiWNPhqqbNF2vXT3u1D50gDvyR11EnvPty7vfQmqfAyqe2o9Odf9W7/wkJpwBelloPSB7/p3X7R7VLOKKmxXKr4Xe/2Md+Sskulunelj1b2bh/3Xck9SKp+Wzr4fO/2L94lpQ+QKt+QPl3Xu33SMik1WzrymnRkY+/2S++TUtKkQy9LVVt6t0/72fHHj5+Vat6Jb3OkSlN+fHz+w2ek+j3x7S6PNPlfj8+X/05qKo9vT8uTJv7L8fm9v5H8B+PbMwdK4//x+Px7v5TaK+PbPaXSxd86Pr/7ISlYH9/uGyWNuv34/M6fSF3++Pa8CdKIW4/P77hHinTGtxdcKg2/RVL/G3vRaFR1RQtV2WJT6OhW5TRtVjAYVDAYVPQvAdHrdeMUSvHqi/l+XVl4WGklaUpLS1NaerrSUlOVMuW7PcbeS1Ktjk/dLtDY81WvlZpO2H8LjT1t+6F6SeKxJyn2uZfu/7O0rY/t+dw7Pp/AY88ebut7e4uMPY65n3PsacrxeT73ercn+tjz3X780apjLwE+98wae6mpV0qy8Niz+ufeWYw9T3iQ1P2lLhYce2f9udfd90mM8AhA0ohGo2r1+xVtalIgEFBHR4cCgYCCwaBer+9Qc9it0sw6TRnQofS0NGVnZx8PiNLSNHrR9+QeMEy2qjf7/s8EAAAAAPRTNqPndRkWsGvXLk2aNMnsMs6J8vJyvnIbpkiGsRcIBFRdXR031dTUqLPzr38F8fl8KiwsjJvy8vLkcDhMrPzsJEPf9Vf0nXXRd/0T/W5d9J110XfWRd8lh8/KWzjzCEBCi0ajamhoUFVVlaqqqnTs2DHV1NSopaUltk73TasnTpwYC4kKCgqUlpZmYuUAAAAAkBwIjwAkjFAopJqamlhQVFVVperqanV1dUmSHA6H8vPzVVpaGnc2kcfj4abVAAAAAHCeEB4BMEUgEIg7m6iyslJ1dXWxbzhLTU1VcXGxJk+erOLiYhUXFys/P18pKXxsAQAAAMCFxG9hAM4bwzDU2tqq+vr62FRXV6eqqqq4y86ys7NVVFSkiy++OBYUeb1e2e12E6sHAAAAAEiERwDOkmEYam9vV0NDQ1xIVF9fr4aGhtglZ9Lxy85yc3NVUlISC4mKiorkdrtN3AMAAAAAwGchPAJwWgzDUFNTk44cOaK6urq4gKijoyO2ns1mU05OjnJzc1VaWqrc3Fzl5eUpLy+Ps4kAAAAAwIIIjwD0KRQK6ejRozp8+LA+/fRTHT58WG1tbZKOB0TZ2dnKy8vT+PHjY+FQXl6efD6fHA6HydUDAAAAAM4VwiMAMgxDLS0t+vTTT2NBUVVVlaLRqCQpLy9PI0aM0JAhQzRkyBBuXA0AAAAA/Qi//QH9UDgcjoVE3Y9+v1+S5HQ6NWjQIF155ZWxsIh7EgEAAABA/0V4BCSZaDSqQCAgv98vv9+v1tZWtba2xp77/X4dPHhQLpdLkuTz+VRaWqohQ4Zo6NChKiws5LIzAAAAAEAM4RFgEV1dXWpvb1d7e7va2triwqGeIVFra2vscrOe0tPT5fF4lJWVpbFjx+qSSy7R0KFDlZWVZcLeAAAAAACsgvAIMEH32UGBQEBtbW0KBAKxYKjnsp5toVCoz9fKzMxUVlaWPB6PCgoKYgFR92P3fM97FJWXl2vUqFEXancBAAAAABZGeGSijF88oRavN25Z6vQpSrt+poxgp/z/trzXNqmzpivt6umKtrSq9ae/6tWedt2XlXrlZYrUNajt4d/0ak+/+Vq5pnxRkaPH1ParJ3q333qjXBPGKHzwsNp/81Tvmm+fL+eoLyhU/pECv1vdqz3zW4uVUjpEXXs+UMcza3u1u7/7DTkGFalrx7vqeO4Pvdvv+pYcA3LV+ebbCr7yeq/2rGXflT07S8HXtqhz45Ze7Z777pQtLVXBl/+ozi07erVn/2yZJKnj2XXqemdPXJstNVWeH98pSQo8s1ahPR/Etds9Wcr61+9Kktp/t1rh8o/i2wfkKPUfv6nW1lYFHluh8MefKhQKqSsUUigUkj8jVe9OKFNra6vGvLNPWW0dcds3eTK0e9QQpaamatr7n2hIV0QpTqecKSlKcToVGTZYXTfOUmZmpry/f0GuzpBSnE7Z22xSW0DOgmHKmH+jJMl/z3IZnZ2x126X5Lp0gtJvmSOJsWflsZe6dqNafvd8fPuAHGXd9feSpPb/flrhjz+Na3cMKpL7u9+QJLX96glFjh6La08ZPlSZ/99tkqTWhx9VtK4xvn3UF5R5+/zj7T/5laL+1rh254Qxyri177EnxY+9lh/+VCfqL2Mv5f39vfpOss7YO9nnXn8Ye7a29j63t8rYs/rnnlljT1MuPt7O516v9kQfe7r9JknWHXuJ8Lln1thzXDlZGjXKsmPP6p97ZzX2BuVLf/njtBXH3tl+7nX3fTIjPEK/ZUgyolFFDUOGYciwSfX19QqHw4o0Ncloa5MRjcowDEWjUUU6O/Thrl0Kh8NKP3BAtsOH/xoOdXWpJcWurfWHJEkTyw/L5w9IOv619k6nUyGnTz6fT0OGDFFpXYcy/O1KSUmR0+k8/viFYbr523+jlJSUvj/Qv/AFZV56qSSpNet1RY34D3QAAAAAAM4Hm2EYhtlFnIldu3Zp0qRJZpdxTnDp0PkTiUTU0tKipqamPqfW1lZFIpGz+hlOpzN2WdiJl4n1XJaRkSG73X6O9uzcYOxZF31nXfSdddF3/RP9bl30nXXRd9ZF3yWHz8pbOPMIlnSqcKilpUU9c1GbzSaPxyOfz6dhw4bJ4/HEzvhJSUmRw+GIzff1/MRlqampSk1Nlc1mM/FdAAAAAADg/EvI8OinP/2p3nvvPdlsNi1btkzjxo0zu6Tz4r/b6+U7sDdu2WXeXF2TV6TOaEQ/O1jea5srcwboqpwC+cMh/eLQ/l7ts3ILNM03QA1dnfr14Y96tV83oFiTsnNUFezQb49+3Kv9poJBGpvl1aGOdv1v5Se92hcWDdGITI8+bPdr5bHDseWGDBlRQ7cOGKgih1Pvtzbr5cYaRaNRRY2ootGojKihG1xueSOG9nV1aFs4oEg0KiMaVSR6fJ0rW4JyBbv0UYpN+zOcx7ePRhWJRhSNRnXxR0ekQIeO5nhUV5DTozKbnC6nZsrQsGHDdDjPq8MZLrlSXUpNTZXL6VLIbtfflx2/f8FLtZX6k78pbt9cdrvuLh0tSXqu+oj2trVI0S6pS1KXlJWSou+XXCRJeubYp/qoPf6ysRynS98ZOkKS9GTlJ/q0oz2uvSg1TX83uEyS9NiRAzrWGYxrH5qeqb8ZOEyS9B+ffqjGUFdc+xcys3Rr0VBJ0v87VKHWcDiu/WJ3tm4uHCxJeuDgPnWd8I1rX/T4dEP+QEnJNfa6fX3gMJWkZ+r91mY9X3O0V/vfDhqu4rR07Wpp1Ct1Vb3a7xjyBeW6UrWtqU4bG2p6tf9TyUh5Upza3FijNxvrerX/sHSUUu0OvVp/TG83N/Rqv6fH2Hv3dMZeDz3H3vqgX6tP6Dsrjb0fn1C71H/GXnko2KvvJOuMPat/7p3N2GuPRvrc3ipjz+qfe2aNvQl/medzz3pjb74ckqw79hLhc8+ssXdpOKJRkmXHntU/985m7BV0dqj7vCMrjr2z/dzr7vtklnDh0TvvvKNPP/1Uq1at0scff6xly5Zp1apVZpd1zkWjUXV0dBz/BizjePgiQ6rpjOigv0PBSFgtrX/5QOnRXhnoUkVtk9qjETW1N8XOrjH+ct+eA02tctk+VXMkrJpIu2QYMgz9ZT1Du49UqzlqU70R0ZGUyPHtooaixvF7+2z8c7neD4ZUazO035MWe92ocTz8+f3GN5XdFlB9qlP7C3Ni4Y5hHP/waPj4eWW2d6jZm6WjQwp77fcLHx1WekenGnOyVTUoP7bcZrPLbrfrkyO1ypJN7bnZiqR55LAfP9vHbj/ePnp0lrKdLn2UlqID6c64cMhut+vvenygd/TxgQ4AAAAAAM5Mwt3z6JFHHlFxcbHmzz9+p/fZs2drzZo1crvdkpLnnkdbtmzRs88+q7S0NNNqsNvtsUuxHA5Hr6k7tOnZ3r3M4XDEXfbldDrjnve1LHZj6B6P3fOJdk+gZMc1ydZF31kXfWdd9F3/RL9bF31nXfSdddF3ycFS9zyqr6/XmDFjYs9zcnJUV1cXC4+k4wPT6rxer2bNmiWXyxV33xybzdbr+Wc9dp+R073dmc5fCJFIRJFIRJ0nfJ0jzBMMBpPi31F/RN9ZF31nXfRd/0S/Wxd9Z130nXXRd8kv4cKjE/V1YlSyJJopKSlJsy+wFv4yYF30nXXRd9ZF3/VP9Lt10XfWRd9ZF32XHHbt2nXStoS7Vig/P1/19fWx57W1tRowYICJFQEAAAAAAPRfCRceXX755dqwYYMk6YMPPlB+fn7cJWsAAAAAAAC4cBLusrWJEydqzJgxWrhwoWw2m/7t3/7N7JIAAAAAAAD6rYQLjyTprrvuMrsEAAAAAAAAKAEvWwMAAAAAAEDiIDwCAAAAAADASREeAQAAAAAA4KQIjwAAAAAAAHBShEcAAAAAAAA4KcIjAAAAAAAAnBThEQAAAAAAAE6K8AgAAAAAAAAnRXgEAAAAAACAkyI8AgAAAAAAwEkRHgEAAAAAAOCkCI8AAAAAAABwUoRHAAAAAAAAOCmbYRiG2UWciV27dpldAgAAAAAAQNKZNGlSn8stFx4BAAAAAADgwuGyNQAAAAAAAJwU4REAAAAAAABOivAIAAAAAAAAJ5VidgGJ7MEHH9SuXbsUDof1rW99S2PHjtUPfvADRSIRDRgwQA899JBcLpfWrVunxx9/XHa7XVOnTtX3v/99hUIh3X333aqqqpLD4dD999+vwYMHx73+ydaJRqP6+c9/rjVr1ujtt9/us7aKigr9+Mc/lt1ul8fj0fLly5Wenq4nn3xSL730kgzD0M0336zbbrvtQrxVOIfMGncnG1M9VVRU6N5775UkjRw5Uvfdd58k6dixY7rjjjs0ZcoU/fCHP7wg71MislrfRSIR3XPPPTp06JBCoZAWLVqkuXPnXqi3K6Ekct/1dUw4evSobrjhBl188cWSJJ/Pp1/+8pcX5s1KMIncdxyrzy+z+v6Pf/yj/vu//1tOp1M5OTl66KGHlJqaGrctx8vPZrW+43j5V4ncdxwvP1si9x3HS4sw0Kft27cbf/u3f2sYhmE0NjYaX/rSl4y7777bWLdunWEYhrF8+XLj6aefNgKBgDFjxgyjtbXViEajxrx584yPPvrIeO6554x7773XMAzD2LJli/G9732v18842Tr/9V//ZTz11FPGpZdeetL6brvtNuO9994zDMMwHnjgAeOpp54yDh8+bHz1q181QqGQ0dnZacyYMcPw+/3n7k3BeWfmuOtrTJ1o8eLFsXX++Z//2di8ebNhGIZx++23Gw8++KDxwAMPnMu3w1Ks2Hevv/668f3vf98wDMPo6OgwLr/8ciMSiZzLt8USEr3v+jomHDlyxLjpppvO4btgTYnedxyrzx8z+/7rX/96rM/uvvtu48UXX+y1LcfLk7Ni33G8PC7R+47j5cklet9xvLQGLls7iUsuuUSPPPKIJMnj8aijo0M7duzQzJkzJUkzZszQ9u3blZ6erhdffFFut1s2m01er1fNzc3avn27rr76aknStGnTtHv37l4/42TrLF68+JSp6qOPPqpx48ZJknJyctTc3KyBAwdqxYoVSklJkcvlUlpamtra2s7NG4ILwsxx19eY6qmrq0uVlZWxdbprkaRf/epXGj58+Ll+OyzFin3n8/nk9/sVjUYVCASUmZkpu73/HRYSue+k0zsm9FeJ3nccq88fM/v+ySefVFZWlsLhsOrq6lRQUBC3HcfLz2bFvuN4eVwi953E8fKzJHrfcby0hv73qXeaHA6HMjIyJElr1qzRlVdeqY6ODrlcLklSbm6u6urqJElut1uStH//flVWVmr8+PGqr69XTk6OJMlut8tms6mrqyvuZ5xsne7X+yzd6wQCAa1du1azZ8+W3W5XZmamJGnr1q3y+XwqKio627cCF1AijLueY6qnpqYmeTye2PO+aunPrNh3EyZMUHFxsWbOnKmvfOUruuuuu87122IJidx3PX/mierr6/WP//iPWrhwoV588cWzfRssySp9x7H63DOz7yXpueee06xZszRkyBBdeumlcdtxvPxsVuw7jpfHJXLf9fyZJ+J4aZ2+43iZ2AiPTmHjxo1as2aN7rnnnrjlhmHEPT906JDuuusuLV++XE6ns9frnLh+X05nnZ4CgYD+4R/+Qd/85jfj/oq1Z88e/exnP9PDDz98Rq+HxGHWuDvZmDrVdvgrK/Xdzp07dezYMb322mt6+eWX9fDDD/f6j0B/YoW+6+b1evW9731Py5cv13/+53/qkUceUW1t7Wltm4wSue84Vp9fZvX9zTffrI0bN6qlpUUvvfTSaW+Hv7JS33G8jGeFvuvG8TJeIvcdx8vER3j0GbZs2aJHH31Ujz32mLKyspSRkfH/t3M3obCFYRzA/7emKWVBaZSklJJSZCMlRNmYBWZlcWSshJWPBZ1QU+ZYT9jaUkpWZIOSIrOW74XFnFiRhmbOee5CzjXMuR9158573P9veeZ9Z868/3d66unMi+fnZwCAaZoIBAIAgEQigZGRERiGgZqaGgBAIBBwurepVAoiAtu2oWkaNE3D7u5u1jFv3d+PdnZ2nLmWZSGdTmN4eBjBYBC9vb3OuNPTU+i6juXlZXZmPSpf+y7bnnq/74qLizP+lvH+XuiV17KLx+NoamqCz+dDaWkpioqKYJrmP1svlaianWVZWe+3sLAQoVDIOXyytrYWV1dXOV0jVamcHWt1buUjexHB/v4+AMDn86GjowMnJyesl3/Ia9mxXv6ganasl7+mcnaslx7xV09Q+kIeHh4kGAzK/f29c03XddnY2BARkUgkImtrayIiMjg4KEdHRxnzNzc3ZXp6WkREtre3ZXx8/NNn/GrMzw7MXlpaksXFxYxr6XRaenp65PLy8ne/Jikmn/su2576KBwOy/HxsYiIDA0NycHBgfPa+vr6f30AqBez29racg4zfHx8lNbWVkkmk3/83b1O9ezevK8Jh4eHMj8/LyIiT09P0tnZKYlE4rfe5ytRPTvW6tzJV/apVEpaWlqc31s0GpWVlZVPc1kv3XkxO9bLV6pn94b18jPVs2O99IZvInyWNpvV1VXEYjFUVlY61wzDgK7reHl5QVlZGaLRKG5vb9Hd3e0c8AUAAwMDaGtrg67ruLm5gd/vh2EYn7qllmVlHROJRHB2doZ4PI6Ghga0t7cjHA5nzG1ubkZ5ebnzGGFjYyPq6+sxNjaG6upqZ9zk5GTGvZHa8rnvsu2p0dHRjLkXFxeYmZmBbduoq6vD1NQUTNPExMQE7u7ukEwmUVFRgdnZWVRVVeVwpdTjxexs28bc3BzOz89h2zb6+/vR1dWVw1VSk+rZZasJmqZB13VcX1/Dsiz09fUhFArlcJXUpHp2rNW5k8/s9/b2EIvF4Pf7UVJSgoWFBRQUFGTMZb1058XsWC9fqZ4d66U71bNjvfQGNo+IiIiIiIiIiMgVzzwiIiIiIiIiIiJXbB4REREREREREZErNo+IiIiIiIiIiMgVm0dEREREREREROSKzSMiIiIiIiIiInLF5hEREREREREREbli84iIiIiIiIiIiFx9B9qYs+doNIaqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#hide_input\n",
    "temp = covid_19.groupby('Date')['Cases'].sum().reset_index()\n",
    "\n",
    "covid = temp['Cases']\n",
    "sars = [8096 for i in range(len(temp))]\n",
    "ebola = [28646 for i in range(len(temp))]\n",
    "mers = [2494 for i in range(len(temp))]\n",
    "h1n1 = [6724149 for i in range(len(temp))]\n",
    "\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "plt.figure(figsize=(20, 8))\n",
    "ax = plt.plot(temp['Date'], covid, label='COVID-19 (2019-2020)', c='#555555', alpha=0.8)\n",
    "ax = plt.plot(temp['Date'], sars, label='SARS (2003-2004)', c='#E71D36', ls='--', alpha=0.8)\n",
    "ax = plt.plot(temp['Date'], ebola, label='EBOLA (2014-2016)', c='#FF9F1C', ls='--', alpha=0.8)\n",
    "ax = plt.plot(temp['Date'], mers, label='MERS', c='#2EC4B6', ls='--', alpha=0.8)\n",
    "plt.title('Number of Cases')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-04-17T06:07:46.220044Z",
     "iopub.status.busy": "2020-04-17T06:07:46.219468Z",
     "iopub.status.idle": "2020-04-17T06:07:46.488244Z",
     "shell.execute_reply": "2020-04-17T06:07:46.487744Z"
    },
    "papermill": {
     "duration": 0.302735,
     "end_time": "2020-04-17T06:07:46.488358",
     "exception": false,
     "start_time": "2020-04-17T06:07:46.185623",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABIoAAAHdCAYAAAByopIdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzdeVSV5f7//+dm2CICKiQ4YOaQYiY44ARKzpppg+VUUR3NTmpWzmbOlVNiOTSYZZqYeg76q46amjkrDkBCGTjbF1EmFQUEmfbvj0770z7gjG42vB5rtZb7vu77ul73vm5dq/e6rnsbTCaTCRERERERERERKfPsrB1ARERERERERERKBhWKREREREREREQEUKFIRERERERERET+S4UiEREREREREREBVCgSEREREREREZH/UqFIREREREREREQAFYpERESkhGjQoAETJkywOHbgwAGCg4OLbYyOHTsSERFRbP3dSHp6Ok899RRdu3bl0qVLFm0LFy7E39+f7t2707FjR7p3787ChQvJycm5qzFPnTrFoUOHgD+/uy5dutxVfyIiIlL2qFAkIiIiJcahQ4f4/fffrR2jWBw9epS0tDS2bNlC5cqVC7V369aNTZs2sW3bNlasWMGxY8cYMmTIXY25detWc6FIRERE5E6oUCQiIiIlxsiRI5kxY0aRbQsXLuTdd98t8nNwcDBffPEF/fr1o3Xr1qxcuZJPP/2U7t2706NHD+Lj483X7d+/n6effprHHnuMjz76yHx869at9OrVi06dOjFw4EAuXrxoHmfixIk899xzLFu2rFCuAwcO8Mwzz9C9e3f69OnDr7/+yrlz5xg9ejQXLlyge/fu5r6up0qVKnz00UecPn2aPXv2ABAZGcmzzz5Lly5d6Nu3r/keCgoKmDZtGt26daNjx46MGTOG3Nxctm3bxuLFi/nmm2+YNWuWue/PPvuMxx9/nM6dO7N//34Ajh07Rr9+/XjiiSfo2rUroaGhN8wnIiIiZYcKRSIiIlJiPP7445hMJjZt2nTb1x46dIiVK1cyc+ZMPvzwQ6pWrcqmTZuoV68ea9euNZ935MgR1q5dy7p161i1ahVxcXHEx8czduxYQkJC+Pnnn2nVqhVTp041X7Nz506++OILXnnlFYsxMzMzeeutt5g4cSKbNm3i1VdfZfTo0VStWpXZs2dTrVo1Nm3ahLu7+03zOzg4EBQUxIEDB8jIyGDIkCGMHDmSn376iZdeeom33noLgJ9++omIiAjWr1/Pjz/+yJEjR9i4cSMdO3akS5cuvPTSS4wfPx6AxMRE6tevz48//siAAQP47LPPAFi0aBH9+/dnw4YNrF69mn379t31tjcREREpHVQoEhERkRJlwoQJzJ07l2vXrt3WdR06dMDBwYH69euTlZVFt27dAKhfvz7Jycnm83r16oW9vT0eHh60aNGCX375hV27dtGyZUvq168PQP/+/dm2bRv5+fkA+Pn5FVnsiYmJoWrVqjRv3hz4czvZpUuXSEhIuKN7d3FxIT09ncjISLy8vAgMDASgZ8+e/L//9/84d+4c3bp1Y+3atTg6OlKuXDkaN25ssWLqf/vr1KkTAI888giJiYkAeHh4sHnzZo4cOULlypX59NNPMRqNd5RZREREShcHawcQERER+btGjRrRokULvv76a5o2bXrL11WoUAEAe3t7i892dnYUFBSYz/t7wcfV1ZUrV65gMpmIiIige/fu5jYXFxfS0tIAqFixYpFjXrx4ETc3N4tjrq6uXLhw4ZZz/11CQgJ16tThypUrxMfHW+QxGo1cvHgRJycn3nvvPX7//XcMBgOpqam8/PLLRfbn4uJi/vPfv4fRo0ezePFi3n77ba5du8Y///lPXnjhhTvKLCIiIqWLCkUiIiJS4owYMYLevXvj7e1tPva/BZ/Lly/fUd9/v+7y5ctUrFgRo9FIQEAACxYsuK2+PDw8zMUkAJPJxOXLl/Hw8ODcuXO31Vd6ejr79u0jODiY3Nxc6tSpw7p16wqdN2nSJBwcHPjPf/6D0Whk1KhRtzUO/FlEGzlyJCNHjiQmJobBgwcTEBBA7dq1b7svERERKV209UxERERKHE9PT1544QUWLlxocezYsWMUFBRw8eJFdu3adUd9b9iwgYKCAi5cuEBkZCT+/v60bduWiIgI8xaumJgY3n///Zv25evrS2pqKr/88ou576pVq1oUuG7FxYsXGT16NK1ataJZs2b4+fmRkpJCdHQ0APHx8YwZMwaTycSFCxeoX78+RqORuLg4fvnlF65evQr8+Z6j9PT0m473+uuvc/z4ceDPrXkuLi4YDIbbyiwiIiKlk1YUiYiISIk0cOBA/v3vf5s/d+/enR9++IHOnTtTp04dunfvfkdbvBo3bsxzzz3HxYsXefnll6lXrx4A7733HsOGDSM3N5cKFSowYcKEm/bl7OzMxx9/zHvvvcfVq1dxd3dn3rx5t1R02bx5M5GRkeTl5VFQUECvXr0YNmwYAE5OTixYsID33nuPzMxMHB0deeuttzAYDAwcOJBx48axbt06/P39GTduHO+++y6+vr506NCB0aNHk5CQcMOtZC+++CKjRo0iNzcXgOeff56HHnroFr49ERERKe0MJpPJZO0QIiIiIiIiIiJifdp6JiIiIiIiIiIigApFIiIiIiIiIiLyXyoUiYiIiIiIiIgIoEKRiIiIiIiIiIj81y396tmcOXPMv8rxz3/+k23btnHkyBEqVaoEwKBBg2jfvj0//PADy5cvx87Ojr59+9KnTx9yc3MZP348586dw97enpkzZ1KzZk3i4uKYOnUqAA0aNGDatGmFxo2MjCy+OxUREREREREREQCaN29e5PGbFor279/P8ePHWbNmDZcuXeKZZ56hdevWjBw5kg4dOpjPu3r1Kp988glhYWE4Ojry3HPP0aVLF7Zv346bmxshISHs2bOHkJAQPv74Yz744AMmTJiAr68vo0aNYufOnTz22GO3HNzWxMbG0rBhQ2vHkDJGz53t0tzZLs2d7dLclV2ae9ulubNdmjvbpbkrHW60MOemW89atGjB/PnzAXBzcyMrK4v8/PxC50VHR9O4cWNcXV1xcnKiWbNmREVFER4eTpcuXQAICAggKiqKnJwcEhIS8PX1BaBDhw6Eh4ff0c2JiIiIiIiIiEjxuOmKInt7e5ydnQEICwsjKCgIe3t7QkND+frrr/Hw8GDSpEmkpqbi7u5uvs7d3Z2UlBSL43Z2dhgMBlJTU3FzczOf6+HhQUpKSpHjx8bG3tUNlhTZ2dml5l7Edui5s12aO9ulubNdmruyS3NvuzR3tktzZ7s0d6XfLb2jCGDr1q2EhYWxdOlSfvvtNypVqkTDhg354osvWLRoEU2bNrU432QyFdlPUcevdy5Qapa0aXmeWIOeO9ulubNdmjvbpbkruzT3tktzZ7s0d7ZLc1c63NXWM4Ddu3fz+eefs2TJElxdXWnTpo35wejYsSPHjh3D09OT1NRU8zXJycl4enri6elpXi2Um5uLyWSiSpUqpKWlmc9NSkrC09Pzjm5ORERERERERESKx00LRenp6cyZM4fFixebf+Vs+PDhxMfHA3DgwAEefvhh/Pz8+PXXX7ly5QqZmZlERUXh7+9PYGAgmzZtAmD79u20atUKR0dH6tSpQ0REBABbtmyhXbt29+oeRURERERERETkFtx069nGjRu5dOkSb7/9tvlY7969efvttylfvjzOzs7MnDkTJycnRo0axaBBgzAYDAwbNgxXV1d69OjBvn37GDBgAEajkVmzZgEwYcIEJk+eTEFBAX5+fgQEBNy7uxQRERERERERkZu6aaGoX79+9OvXr9DxZ555ptCx7t270717d4tj9vb2zJw5s9C59erV49tvv72drCIiIiIiIiIicg/d0juKRERERERERESk9LvlXz2T/3PmzBlmzJjBxYsXKSgooGnTpowbNw6j0UhWVhYzZ84kJiYGBwcHHnjgAaZMmQL8uTpr0qRJPProo+a+QkJCqFy5MpmZmVSuXJkXX3yR4OBgrl69irOzM7m5uQQGBjJ06FDs7e0LZTl27BhDhw7llVde4cUXXwTg5MmTTJ48GYPBwEMPPcTUqVNxcLCc6szMTIYMGcLChQuJjY1l3rx52NnZUbt2bT744APs7OyYMWMG0dHRGAwGJkyYgK+vLwDffPMNs2fP5uDBg1SoUAGA1atX8+9//xtHR0f+8Y9/0K1bt0JZN27cyNKlS7Gzs6NNmzaMGDGC3Nxcxo8fz7lz58yrz2rWrElcXBzTp0/Hzs4ONzc3QkJCKF++PF9++SWbNm3CYDDwxhtv0LZtW1577TWmT59OjRo1imeCRURERERERMoorSi6Tfn5+QwfPpxXX32VsLAw1q5dC8Ann3wCwMyZM/H09OS7774jLCyMwYMH8+qrr5KXl0fPnj358ccfLfrbsmULTzzxRKFxZs6cyYoVK/jmm29ITk7mo48+KnTO1atXee+992jTpo3F8blz5/Laa68RGhpKtWrVCo0JsGjRIvr27UvFihWZPHkyCxYsYPXq1WRmZrJ7924OHjzIH3/8wZo1a/jggw/44IMPAPjuu++4cOGCxa/UXbhwgaVLl/Ltt9+yfPlyvv76a7Kzsy3Gy8rKYu7cuSxbtow1a9awb98+Tpw4wfr163Fzc2PVqlW8/vrrhISEAPD+++8zfvx4QkNDqVWrFuvWrSM+Pp6NGzfy7bffsnjxYvOWxlGjRvHee+/deOJERERERERE5KZUKLpNe/fupU6dOrRs2RIAg8HAmDFjGDZsGBkZGezevZvXX3/dfH7z5s3x9fXl4MGD9OjRg59++snc9ttvv+Hp6YmXl9d1xzMajbzzzjv88MMP5ObmFmpbsmSJRdEG4I8//jCv/mnXrh179+61aL927RqbN2/m8ccfB2DdunVUrVoVAHd3dy5dukR4eDidO3cGoG7duly+fJmMjAw6d+7MiBEjMBgM5v4SEhKoU6cO5cqVo1y5cvj4+BAdHW0xZvny5fnhhx9wcXHBYDBQqVIl0tLSCA8Pp0uXLgAEBAQQFRUFwOeff26+B3d3d9LS0jhw4ADt2rXDaDTi7u5OjRo1OHHiBI888giXL1/mjz/+uO73KCIiIiIiIiI3Z9Nbz6Kiojh06FCx9tmiRQuaNWt23fZTp07RsGFDi2NOTk7An1u+6tSpU2ibV8OGDTlz5gweHh7UrFmTmJgYfH19+fHHH+nVq9dNMzk7O1OtWjXOnz/Pgw8+aD7u4OBQaCyA+vXrs3PnTp5++ml2795NamqqRXtMTAz169c3b2VzcXEBIDk5mb179/LWW28xb948GjVqZL7G3d2dlJQUateuXWi8Bx98kGPHjnHx4kXKlSvHL7/8Yi6k/d1f4xw9epSEhAT8/Pz49NNPcXd3B8DOzg6DwUBOTo753KtXr/L9998zf/58fvrpJ/O5f8/UoEEDWrRowYEDB6hVq9ZNv08RERERERERKZpWFN0mg8FAfn7+bbWZTCbs7P78qnv27MnGjRsB2LZtW5Hv8ilKZmamuY+bGTduHD/++CMvvfQSJpMJk8lk0Z6cnGxeQfSXCxcu8PrrrzNlyhQqV65c5D1cT6VKlRgzZgxDhw5l/Pjx1KtX77rnnzlzhtGjRxMSEoKjo+MNx7l69SpDhgxh4MCB1K1b94bnenl5cf78+etmFBEREREREZGbs+kVRc2aNbvh6p97oU6dOqxcudLiWE5ODmfOnMHb25vTp0+Tk5OD0Wg0t8fFxeHj4wNAly5d+Pzzz3niiSd46KGHqFix4k3HvHz5MleuXKF69eoMGTKEjIwMnnzySfr06VPk+dWqVWPx4sUA7N69m+Tk5ELn/H3rWEZGBoMHD+btt9+mbdu2AHh6elqsREpOTqZKlSrXzfj444+bt7KNHDmSGjVqsGDBAg4dOkT9+vWZNGkSiYmJDBs2jDlz5phXZXl6epKSkoKPjw+5ubmYTCaMRiN5eXkMHTqUnj170rt3b/O5p0+fNo+ZlJRUaNudiIiIiIiIiNw5rSi6TYGBgSQkJLBt2zYACgoK+PDDD9m4cSMuLi506NCBRYsWmc+Piori999/p3nz5sCf268aNGjA4sWLb2nbWV5eHjNmzOCll17Czs6Ozz77jBUrVly3SASwYMECduzYAfz5/qGOHTtatHt6epKYmGj+PGvWLF5++WWCgoIs7nPz5s0AHDlyBE9PT/N2sKIyBgcHc+3aNVJSUoiNjeXRRx/lzTffZMWKFUyaNAmAd999l6lTp1psaQsMDGTTpk0AbN++nVatWgGwZMkSWrZsaXGfrVu3ZseOHeTk5JCUlERycjL16tUDil4lJSIiIiIiIiK3x6ZXFFmDnZ0dX331FZMnT2bRokUYjUYCAgJ44403AJgwYQIhISE8+eST5pcuz58/n6ysLHMfvXr1YuzYscydO/e647zzzjuUL1+ey5cv0759e/7xj38UOue3335j9uzZJCQk4ODgwObNm1m4cCE9e/Zk7NixLFy4EH9/f9q3b29xna+vL0ePHiU/P5+cnBy+++47/vjjD8LCwoA/t8f169ePRo0a0b9/fwwGA1OmTAHgs88+Y9++faSkpDB48GCaNGnC2LFj6d69O/369cNgMDB58uRC7046ffo0ERERLFiwwHzslVdeoUePHuzbt48BAwZgNBqZNWsWACtXrsTb25vw8HAAWrVqxRtvvEHfvn158cUXMRgMTJ061bwd79ChQ8yYMeOW5lBEREREREREimYw3ejlM1YWGRlpXolj62JjYwu9BNuaZs6ciZ+fHz169LB2lLsWFxfHvHnz+OKLL6wdpcQpac+d3DrNne3S3NkuzV3Zpbm3XZo726W5s12au9LhRvUWbT0ro4YPH86aNWu4fPmytaPclfz8fD788EPz9jYRERERERGR4nb27Fni4uKsHeO+0NazMsrFxYXly5dbO8Zds7e356uvvrJ2DBERERERESnF1q5dS4UKFcw/VFWaaUWRiIiIiIiIiMh1JCcnc/78+TJRJAIVikRERERERERErismJgaDwUDjxo2tHeW+UKFIRERERERERKQIJpOJ6OhoHnroISpWrGjtOPeFCkUiIiIiIiIiIkU4f/48KSkp+Pn5WTvKfaOXWd+BlStX8v3332M0GsnOzmbkyJEEBASY2wcNGkS5cuX49NNPzccGDx7Mgw8+iL29PQUFBTg5OTFjxgy8vLxITExk0qRJZGVlkZ2dzcMPP8y0adMwGo0W4yYlJTF+/HgWL17M1q1bWbp0KXZ2drRp04YRI0aQm5vL+PHjOXfuHPb29sycOZOaNWsSFxfH1KlTAWjQoAHTpk2joKCA6dOnc/ToUfLy8ujbty99+vQpdK/Lly/nP//5DyaTid69e/PCCy+Qnp7OqFGjSE9Px9nZmZCQECpVqsS+ffuYN28e9vb2BAUFMWzYMHM/2dnZ9OzZk6FDh9K1a1eGDBnCp59+iqurazHPjoiIiIiIiEjxiI6OxmAw8Oijj1o7yn2jFUW36ezZs/zrX/9i5cqVhIaGMnfuXIuC0IULFzh58iRRUVGkp6dbXLtkyRJWrFjBypUreeKJJ5g/fz4A8+fPp3fv3oSGhhIWFoajoyO7d+8uNPYHH3zA8OHDyc/PZ+7cuSxbtow1a9awb98+Tpw4wfr163Fzc2PVqlW8/vrrhISEmK+bMGECq1evJiMjg507dxIVFYWDgwOrVq1i2bJlzJs3j4KCAovx4uPjWbduHatXr2bVqlV89dVXpKens3z5clq2bMmqVavo2rUrS5YsAeD9999n4cKFrFq1ir1793LixAlzX5999pl5mZ6LiwsvvfQSH330UTHMiIiIiIiIiEjx+2vb2cMPP4yLi4u149w3KhTdpoyMDK5du0Zubi4ADz30EKGhoeb2jRs30qFDBwIDA9myZct1+/Hz8+OPP/4A4MqVK2RkZJjbpk+fTqdOnSzOP3fuHPHx8TRr1ozy5cvzww8/4OLigsFgoFKlSqSlpREeHk6XLl0ACAgIICoqipycHBISEvD19QWgQ4cOhIeH4+/vz8SJEwG4ePEiFStWxM7O8nGoUaMG3377LQ4ODhiNRpycnMjIyLAY56/+4uPjqVixItWqVcPOzo7HHnuM8PBwAE6ePMmJEydo3769ue/OnTuzd+9eMjMzb/3LFxEREREREblP4uPjSUtLK1PbzqAUbD27PG5GoWPl2rXCqWcnTNnXuDIlpHB753Y4dWlHweV00mcstGirOHvCDcfz8fHB19eXTp068dhjjxEUFETXrl1xcPjzq1y/fj1jxowhPT2d0NBQnn322SL72bRpE4888gjw57a0oUOHsm7dOgIDA+nVqxe1atWyOP/gwYM0b97c/PmvaubRo0dJSEjAz8+PTz/9FHd3dwDs7OwwGAykpqbi5uZmvs7Dw4OUlBTz5zfffJOoqCg+/PDDQhnt7OyoUKECAHv27KFy5cpUq1aN1NRU8zgeHh4kJyeTkpJiPgbg7u5OfHw8ALNnz2bSpEl899135va/lu4dPnyYwMDAG37nIiIiIiIiIvdbdHQ09vb2NGrUyNpR7iutKLoDc+bMITQ0FB8fH7788kv+8Y9/YDKZiI+PJykpiebNm9O2bVvi4uK4ePGi+brBgwcTHBxM+/btOXXqFG+99RYATZo04eeff2bQoEEkJyfz3HPPsWfPHosxk5OT8fLysjh25swZRo8eTUhICI6OjoVymkymmx5bsGABa9asYdq0aRarmv7u8OHDzJ49m7lz597SGH/33Xff0aRJE2rWrFmozcvLi/Pnz9/wehEREREREZH7raCggJiYGBo0aICTk5O149xXNr+i6EYrgAxO5W7YblfR9aYriP6XyWQiJyeHunXrUrduXYKDg3n88cc5d+4c69ev59q1azz99NMA5OXl8eOPP/LCCy8Af76jqEKFCoSGhnLmzBnzqqDs7GzKly9P586d6dy5M02bNmXDhg20bdvW8n4MBvOfExMTGTZsGHPmzKFhw4YAeHp6kpKSgo+PD7m5uZhMJqpUqUJaWpr5uqSkJDw9PTl58iQAdevWpUaNGtSsWZNTp04RFhbG6dOnCQgIYMiQIcTFxTFx4kQ+//xzqlWrZjGOq6uruT9PT09SU1MLjbNjxw7i4+PZsWMHiYmJGI1GqlatavHybxEREREREZGS5MyZM6Snp5e5bWegFUW3LSwsjEmTJplX0qSnp1NQUICHhwcbNmxg2bJlfP/993z//fcsWrSIDRs2FOqjf//+HDx4kLi4OAoKCujVq5fFi58TExPx9va2uMbT05PExETz53fffZepU6daLIELDAxk06ZNAGzfvp1WrVrh6OhInTp1iIiIAGDLli20a9eOU6dOMW/ePACysrI4ffo03t7eTJ8+nRUrVjBkyBDy8/OZMGECCxYssMjz93H+6s/b25uMjAzOnj1LXl4e27dvJzAwkI8//pi1a9fyr3/9iz59+jB06FBzkSgpKYmqVave+WSIiIiIiIiI3AOHDx/G0dHRvDCjLLH5FUX3W+/evTl16hR9+vTB2dmZvLw8Jk6cyJkzZzAajTRo0MB8rr+/PxcuXCi0vcrBwYGxY8cydepUVq1aRUhIiPnn6wG8vb2ZPHmyxTUtWrTgm2++AeD06dNERESwYMECc/srr7xCjx492LdvHwMGDMBoNDJr1iwAJkyYwOTJkykoKMDPz4+AgABMJhP79++nf//+5OTk8Nprr1m8YwggPDycs2fPMmXKFPOxMWPGEBwczJgxY3j++edxc3Mzv99o6tSpjBo1CoAePXpQu3bt636PJpOJ3377jenTp9/0OxcRERERERG5X/Lz8/ntt9945JFHMBqN1o5z3xlMN3vJjBVFRkZavMDZlsXGxt51JfKNN95g8ODBpWLp29atW9mzZ49FgUyKX3E8d2IdmjvbpbmzXZq7sktzb7s0d7ZLc2e7ysLcHT16lK+//pqXXnrJ/CNUpc2N6i3aemZDJk6cyPz588nJybF2lLuSkZHB8uXLGTFihLWjiIiIiIiIiFiIjo7GycmJ+vXrWzuKVWjrmQ2pWrUqS5cutXaMu+bi4sKKFSusHUNERERERETEQl5eHkeOHKFx48Y4OJTNkolWFImIiIiIiIiIAHFxcVy7dg1fX19rR7EaFYpERERERERERICYmBgqVKhAvXr1rB3FalQoEhEREREREZEy79q1a8TGxtK4cWPs7MpuuaTs3rmIiIiIiIiIyH/FxsaSm5tbKn5p/G6oUHSbzp49S9OmTQkODrb4Ly0tjXXr1vHYY48RHBzMiy++yMCBA0lNTTVfu2zZMp555hn69etHv379OHTokLmtY8eOZGZmFjnm4sWLad26NXl5eUW2x8XFMXz4cACWL1/Oc889x7PPPsvKlSsBSE9P57XXXmPAgAEMGjSItLQ04M9q6bhx4+jdu3ehPrOzs+ncuTPr1q0rcsw5c+bQr18/nn32WbZs2QLA+fPnCQ4O5vnnn+ett94y/zrb5cuXGTRoEG+++aZFH1999RVPPfUUzz77LDExMSQmJjJw4EByc3OLHFNERERERETkXomOjqZixYrUqlXL2lGsSoWiO1C7dm1WrFhh8V+lSpUA6NGjBytWrCA0NJRmzZqxdu1aAHbv3s3evXtZtWoVa9asYcGCBUybNo1Tp07ddLz169dTqVIl9u3bV2T7lClTeOedd4iPj2fdunWsXr2aVatW8dVXX5Gens7y5ctp2bIlq1atomvXrixZsgT4s9jTsGHDIvv87LPPqFixYpFt+/fv5/jx46xZs4Yvv/ySGTNmALBgwQKef/55vv32W2rVqkVYWJg5X/PmzS36OH78OBs2bGDt2rVMnz6dHTt2ULVqVYKCgvjmm29u+p2IiIiIiIiIFJerV69y7NixMr/tDFQouqcuXLiAl5cXAP/5z38YN24cTk5OAHh5efHqq68SGhp6wz6OHj1KQUEBAwcOZMOGDYXaIyIi8PDwoHr16tSoUYNvv/0WBwcHjEYjTk5OZGRkEB4eTpcuXQDo0KED4eHhAIwYMYLOnTsX6vPkyUIwVVIAACAASURBVJOcOHGC9u3bF5mpRYsWzJ8/HwA3NzeysrLIz8/nwIEDdOrUqdA477//fqFC0fbt23n88cdxcHCgUaNG5tVGffv2ZfXq1Tf8TkRERERERESK0++//05+fj5NmjSxdhSrc7B2gLu2b1zhY9XbwUM9IS8bDk4p3F6zM9TsAtcuQ+QMy7aA2XcVZ+PGjfz2229cunSJChUqMHbsWACSk5OpW7euxbk+Pj58//33N+xv/fr19OjRg65duzJv3jyuXbtGuXLlzO379++nRYsWANjZ2VGhQgUA9uzZQ+XKlalWrRqpqam4u7sD4OHhQXJyMgAuLi7mbWh/N3v2bCZNmsR3331XZCZ7e3ucnZ0BCAsLIygoCHt7e7KysjAajeZxUlJSzOP8r4SEBOzt7Rk0aBB5eXm88847+Pj44OzsjIeHB2fOnOGhhx664XcjIiIiIiIiUhwOHz6Mu7s7NWrUsHYUq9OKojtw+vRpi/cTTZ482dz219az9evXM2DAAIu2otxoSZvJZGLDhg307NmTSpUq0aRJE3bu3GlxTnJysnnV0l8OHz7M7NmzmTt3bpF93sh3331HkyZNqFmz5g3PA9i6dSthYWFF3uPNxjGZTOTn5/Pll18yfPhw3n33XXObl5cX58+fv+n4IiIiIiIiIncrIyODkydP4ufnh8FgsHYcq7P9FUU3WgHk4HTj9nIV72gF0V/vKLqZbt26mbdoeXl5ERcXZ/FOoNjYWOrVq3fd66Oiorhw4YJ5W1Z6ejobNmyga9euFuf9/UGOi4tj4sSJfP7551SrVg0AT09PUlJScHV1JSkpCU9Pz+uOuWPHDuLj49mxYweJiYkYjUaqVq3KihUryMjI4Mknn6RPnz7s3r2bzz//nC+//BJXV1cAnJ2dyc7OxsnJ6abjPPDAA9SpUweDwYC/vz8JCQnXPVdERERERETkXomJicFkMpX5Xzv7i+0Xikqw6OhoateuDUCvXr2YPXs2n332GeXLlyc5OZmlS5fy6aefXvf69evXM3r0aIKDg4E/X67VuXNnMjMzzVvMPD09SUpKAiA/P58JEyawYMECvL29zf0EBgayadMmhg4dypYtW2jXrt11x/z444/Nf164cCE1atQgICCAgIAA8/H09HTmzJnDsmXLzC/xBggICGDz5s089dRTNx0nKCiI1atX07NnT06ePGkuagEkJSVRtWrV614rIiIiIiIiUlxiYmLw9PTU/4f+lwpFd+CvrWd/N2bMGOD/3lH0l6lTpwLQtm1bKleuTP/+/SlXrhwGg4ExY8ZYbPEaPHgw9vb2APTs2ZNt27ZZ/KS8s7Mz7du35+eff+bJJ58EoHXr1ixbtoxXXnmF8PBwzp49y5Qp//depjFjxhAcHMyYMWN4/vnncXNz48MPPwTgzTffJDEx0Xw/ffv2pVevXje9/40bN3Lp0iXefvtt87HZs2czfPhwxo0bx5o1a6hevTpPP/00+fn5vPLKK1y5coWkpCSCg4MZOnQobdq0YdeuXfTr1w/AvH0tKyuL1NRUc4FNRERERERE5F5JS0vjzJkzhXbulGUG081eJmNFkZGRhX4ty1bFxsZe96fo71bfvn2ZP3++xaocW7V8+XJycnIYPHiwtaOUCvfyuZN7S3NnuzR3tktzV3Zp7m2X5s52ae5sV2mbu127drFx40ZGjx7NAw88YO04982N6i16mXUpMG3aNGbOnGntGHctMTGRHTt28PLLL1s7ioiIiIiIiJQBMTEx1KhRo0wViW5GhaJSoGHDhixYsMDaMe5a1apV+frrrzEajdaOIiIiIiIiIqVcamoqZ8+e1Uus/4cKRSIiIiIiIiJS5sTExADg6+tr5SQliwpFIiIiIiIiIlLmHD58mIceesji17xFhSIRERERERERKWMSExNJTk7WaqIiqFAkIiIiIiIiImVKdHQ0BoNBhaIiOFg7gC06e/YsnTp1Ys2aNTRp0sR8/Nlnn+Xhhx8G4MiRIxbL1xo1akTDhg3p2LEjVatWxd7enoKCApycnJgxYwZeXl4kJiYyadIksrKyyM7O5uGHH2batGl6ubOIiIiIiIhIMTGZTERHR1O3bl1cXFysHafEUaHoDtWsWZP169ebC0V//PEHV65cMbePHDmSDh06mD/Hxsaa/7xkyRIqVKgAwLp165g/fz4zZsxg/vz59O7dm8cffxyAyZMns3v3bjp16nQ/bklERERERESk1EtISODixYsW/88u/0eFojvk5+fHvn37yM/Px97eng0bNhAYGEh2dvZt97N27VoArly5QkZGhrlt+vTpxZpZREREREREpKyLjo7G3t6eRo0aWTtKiWTzhaLpJ34rdKx1JQ+6PlCNawX5zD4VW6g9yL0K7d29uJKXy8dnjlq0Ta736C2N6+joiJ+fHwcOHCAgIICff/6ZN954g82bN99W/k2bNvHII48AMHjwYIYOHcq6desIDAykV69e1KpV67b6ExEREREREZGiFRQUEBMTQ/369XF2drZ2nBLJ5gtF1tS9e3fWr1/PAw88gJeXl8VDNm/ePJYuXWr+3Lt3bxo2bAj8WRCyt7cnPj6e5s2bM23aNACaNGnCzz//zN69e9m1axfPPfccH330EW3btr2/NyYiIiIiIiJSCv3xxx9cvnzZ/MoXKczmC0U3WgFUzs7+hu1uDo63vIKoKG3atGH69OlUqVKFbt26WbTdyjuKQkNDOXPmjPnlWdnZ2ZQvX57OnTvTuXNnmjZtyoYNG1QoEhERERERESkG0dHRODo6mhdySGF21g5gy4xGIy1atGDt2rV07Njxtq/v378/Bw8eJC4ujoKCAnr16sWJEyfM7YmJiXh7exdnZBEREREREZEyqaCggF9//RUfHx/KlStn7Tglls2vKLK27t27c/HiRVxdXS2O/+/WM3d3d+bPn29xjoODA2PHjmXq1KmsWrWKkJAQpk6dam739vZm8uTJ9zS/iIiIiIiISFlw8uRJMjMz8fPzs3aUEk2Fojvg7e3NrFmzAGjfvj3t27cHoFWrVrRq1arIa/7aerZt2zaL423btjVvLfP19SU0NPQepRYREREREREpu6KjoylXrhwNGjSwdpQSTVvPRERERERERKRUy8vL48iRIzRq1AhHR0drxynRVCgSERERERERkVLt2LFjZGVladvZLVChSERERERERERKtV9++QVnZ2fq1atn7SglngpFIiIiIiIiIlJqXb16ld9//50mTZpgb29v7TglngpFIiIiIiIiIlJqRUdHk5+fj7+/v7Wj2AQVikRERERERESk1IqMjKRatWpUr17d2lFsggpFd+Ds2bP07t3b4tjChQsJDQ3l8uXLDBo0iDfffNPcduDAAfr3709KSorF+QcOHADg/Pnz9O7dm9mzZ9+fGxAREREREREpAxITEzl79izNmze3dhSboUJRMZsyZUqRD6CXlxeLFi0q8poJEybQpk2bex1NREREREREpEyJiorCzs6OJk2aWDuKzVChqJi9//77RRaKWrduzdGjRzl9+nShtoULF1K3bt37EU9ERERERESkTCgoKCAqKgofHx9cXFysHcdmOFg7wN1696PfCx0LbO5BjyAvruXkM/2To4XaO7auQqc2VbiSkcvsJcct2j4Y8cgtjXv69GmCg4PNnxMSEhg4cOANH74RI0Ywb948Fi5caHFcD6yIiIiIiIhI8Tp27BgZGRnadnabbL5QZC21a9dmxYoV5s//W/wpSqtWrVi6dCmHDx++l9FEREREREREyryIiAgqVKiAj4+PtaPYFJsvFN1oBVA5o/0N291cHG95BVFxGTlyJO+//z4tW7a8r+OKiIiIiIiIlBVXr14lNjaWNm3aYG9vb+04NkXvKLrPGjRoQI0aNdi+fbu1o4iIiIiIiIiUSocPHyY/P59mzZpZO4rNsfkVRSVJfn4+wcHBXLlyhaSkJIKDgxk6dCh2dpb1uLfeeotu3boBkJSUxOjRo0lJSSErK4vffvuNKVOmUK9ePWvcgoiIiIiIiIjNi4yMpFq1alSvXt3aUWyOCkV3wNvbm3Xr1lkcGz58OAAvv/xykde4ubmZ/1ytWjViYmLMn//+riMRERERERERuXOJiYkkJCTQs2dPa0exSdp6JiIiIiIiIiKlRkREBPb29jRt2tTaUWySCkUiIiIiIiIiUirk5+dz+PBhfHx8qFChgrXj2CQVikRERERERESkVDh69CgZGRk0b97c2lFs1i29o2jOnDlERkaSl5fHP//5Txo3bszYsWPJz8+nSpUqfPjhhxiNRn744QeWL1+OnZ0dffv2pU+fPuTm5jJ+/HjOnTuHvb09M2fOpGbNmsTFxTF16lTgz18CmzZt2r28TxEREREREREp5aKionBxcaFBgwbWjmKzbrqiaP/+/Rw/fpw1a9bw5ZdfMmPGDBYsWMDzzz/Pt99+S61atQgLC+Pq1at88sknLFu2jBUrVrB8+XLS0tJYv349bm5urFq1itdff52QkBAAPvjgAyZMmMDq1avJyMhg586d9/xmRURERERERKR0yszMJDY2liZNmmBvb2/tODbrpoWiFi1aMH/+fODPX+7KysriwIEDdOrUCYAOHToQHh5OdHQ0jRs3xtXVFScnJ5o1a0ZUVBTh4eF06dIFgICAAKKiosjJySEhIQFfX1+LPkRERERERERE7kR0dDT5+fnadnaXbloosre3x9nZGYCwsDCCgoLIysrCaDQC4OHhQUpKCqmpqbi7u5uvc3d3L3Tczs4Og8FAamqqxc/F/9WHiIiIiIiIiMidiIiIoHr16lSrVs3aUWzaLb2jCGDr1q2EhYWxdOlSunbtaj5uMpmKPP92jl/vXIDY2NhbjViiZWdnl5p7Eduh5852ae5sl+bOdmnuyi7Nve3S3NkuzZ3tKqlzl5qayqlTpwgMDCyR+WzJLRWKdu/ezeeff86XX36Jq6srzs7OZGdn4+TkRFJSEp6ennh6epKammq+Jjk5mSZNmuDp6UlKSgo+Pj7k5uZiMpmoUqUKaWlp5nP/6qMoDRs2vMtbLBliY2NLzb2I7dBzZ7s0d7ZLc2e7NHdll+bedmnubJfmznaV1Llbv349FSpUoEePHlSoUMHacUq8yMjI67bddOtZeno6c+bMYfHixVSqVAn4811DmzdvBmDLli20a9cOPz8/fv31V65cuUJmZiZRUVH4+/sTGBjIpk2bANi+fTutWrXC0dGROnXqEBERYdGHiIiIiIiIiMjtyM/P55dffqFhw4YqEhWDm64o2rhxI5cuXeLtt982H5s1axYTJ05kzZo1VK9enaeffhpHR0dGjRrFoEGDMBgMDBs2DFdXV3r06MG+ffsYMGAARqORWbNmATBhwgQmT55MQUEBfn5+BAQE3Lu7FBEREREREZFS6ejRo2RmZuol1sXEYLrRC4KsLDIystRM9LAp+8wrsv4S2NyDHkFeXMvJZ/onRwtd07F1FTq1qcKVjFxmLzleqL17Oy/a+XuQeukaHy07Waj9qU7VaOlbmYSkLD799nSh9r6P18DPpyKnz2by5b//KNQe/FRNfOq4EncqnRXfxxdqf7VPLWp7VyA67jL/+jGhUPvQ52tTw6s8B2Mu8f3P5wu1j3ilLg9ULsfuiAts2p1UqH3c4Idxc3Hk5/AUtu0v/LLzycMaUM5oz8ZdSeyNvFCo/YMRjwDw/209R8SvaRZt5Yx2TB7mA8CaH88SE3fFot3NxYFxg+sD8M33/4+jpzIs2h+obGTEK/UA+DLsDKfjr1q01/ByYujzdQD49NtTJCRlW7TXrunMq889BMBHy06QeinHor1BHRdeeupBAGYvOcaVjDyLdl8fN/o97g3A9E/iuJZTYNHu37gSz3SuTmxsLKGbCv8V17NX8p+92NhYDh2rYLPPHsC7H/1e6LspC8/elYtnSM+tarPPHtj2v3tw589e9Uqp1KhZz2afPVv/dw+s9+y1qJ9Jw4YN9e+eDT57L3Y30LBhQ5t99qz97541n73Axrn06Oxns8+erf+7dzfPXnWPqwx7yR8oOc/e8ePHycjIoEmTJvTr4X3P/t37a95LgxvVW2669UxEREREREREpCTKzcslLS0Njwc8MBgM1o5TKmhF0X1SUl/4JaWbnjvbpbmzXZo726W5K7s097ZLc2e7NHe2q6TN3Z49e1i/fj1vv/02VatWtXYcm6EVRSIiIiIiIiJS6kRGRuLt7a0iUTFSoUhEREREREREbM65c+c4f/48zZo1s3aUUkWFIhERERERERGxOZGRkdjb29OkSRNrRylVVCgSEREREREREZuSl5fH4cOHadiwIc7OztaOU6qoUCQiIiIiIiIiNuXo0aNkZmbi7+9v7SiljgpFIiIiIiIiImJTIiMjcXV1pX79+taOUuqoUCQiIiIiIiIiNiMjI4O4uDiaNm2KnZ3KGsVN36iIiIiIiIiI2IzDhw9TUFBA8+bNrR2lVFKhSERERERERERsgslkIiIiAm9vb7y8vKwdp1RSoUhEREREREREbML58+dJTEzUaqJ7SIUiEREREREREbEJERER2Nvb4+fnZ+0opZYKRSIiIiIiIiJS4uXl5XH48GEaNWqEs7OzteOUWioUiYiIiIiIiEiJFxcXx9WrV7Xt7B5ToUhERERERERESrzIyEhcXV15+OGHrR2lVFOhSERERERERERKtIyMDOLi4mjatCl2dipl3Ev6dkVERERERESkRDt48CAmkwl/f39rRyn1VCgSERERERERkRIrLy+P8PBw6tevj6enp7XjlHoqFImIiIiIiIhIiXX48GHS09MJCgqydpQyQYUiERERERERESmRTCYTu3btolq1atStW9faccoEFYpEREREREREpEQ6duwYycnJtGvXDoPBYO04ZYIKRSIiIiIiIiJSIu3atYuKFSvi5+dn7ShlhgpFIiIiIiIiIlLiJCQkcPLkSQIDA7G3t7d2nDJDhSIRERERERERKXF2795NuXLlaNmypbWjlCkqFImIiIiIiIhIiZKWlkZ0dDQtW7bEycnJ2nHKFBWKRERERERERKRE2bNnDwaDgcDAQGtHKXNUKBIRERERERGREiMrK4tDhw7h6+tLpUqVrB2nzFGhSERERERERERKjIMHD3Lt2jWCgoKsHaVMUqFIREREREREREqEvLw89u3bR7169ahevbq145RJKhSJiIiIiIiISIkQExPD5cuXadeunbWjlFkqFImIiIiIiIiI1ZlMJnbt2oWXlxf169e3dpwyS4UiEREREREREbG6EydOkJiYSLt27TAYDNaOU2apUCQiIiIiIiIiVrdr1y5cXV1p0qSJtaOUaSoUiYiIiIiIiIhVnT9/nuPHjxMYGIiDg4O145RpKhSJiIiIiIiIiFXt3r0bo9FIy5YtrR2lzFOhSERERERERESs5vLlyxw+fBh/f3+cnZ2tHafMU6FIRERERERERKxm7969mEwm2rZta+0oggpFIiIiIiIiImIl2dnZHDhwgMaNG+Pu7m7tOIIKRSIiIiIiIiJiJREREVy7do2goCBrR5H/UqFIRERERERERO67/Px89uzZQ+3atfH29rZ2HPkvFYpERERERERE5L779ddfSUtLo127dtaOIn+jQpGIiIiIiIiI3Fcmk4ldu3bxwAMP4OPjY+048jcqFImIiIiIiIjIfXXq1CnOnTtHUFAQdnYqTZQkmg0RERERERERua927dqFi4sLzZo1s3YU+R8qFImIiIiIiIjIfZOUlMTRo0dp06YNDg4O1o4j/0OFIhERERERERG5b3bv3o2joyOtW7e2dhQpggpFIiIiIiIiInJfpKen88svv9C8eXMqVKhg7ThSBBWKREREREREROS+2LdvHwUFBbRt29baUeQ6VCgSERERERERkXsuJyeH/fv306hRIx544AFrx5HrUKFIRERERERERO65iIgIsrKyaNeunbWjyA2oUCQiIiIiIiIi91RBQQG7d+/mwQcfpFatWtaOIzegQpGIiIiIiIiI3FOHDx/m0qVLBAUFWTuK3IQKRSIiIiIiIiJyz+Tn57N161aqVavGI488Yu04chMqFImIiIiIiIjIPRMZGcnFixfp2rUrdnYqQ5R0miERERERERERuSfy8vL4+eefefDBB/Hx8bF2HLkFKhSJiIiIiIiIyD1x4MABLl++TNeuXTEYDNaOI7dAhSIRERERERERKXY5OTls376dOnXqUK9ePWvHkVukQpGIiIiIiIiIFLt9+/aRkZFBt27drB1FboMKRSIiIiIiIiJSrLKzs9m5cycNGjSgVq1a1o4jt0GFIhEREREREREpVrt37yYrK0uriWyQCkUiIiIiIiIiUmwyMzPZs2cPjz76KNWrV7d2HLlNKhSJiIiIiIiISLHZuXMnOTk5dOnSxdpR5A6oUCQiIiIiIiIixeLKlSuEh4fTpEkTvLy8rB1H7oAKRSIiIiIiIiJSLLZv305+fj6dO3e2dhS5QyoUiYiIiIiIiMhdu3TpEgcPHsTf3x8PDw9rx5E7pEKRiIiIiIiIiNy1n3/+GYPBQKdOnawdRe6CCkUiIiIiIiIicldSU1OJjIykVatWVKxY0dpx5C6oUCQiIiIiIiIid+Wnn37CwcGBDh06WDuK3CUVikRERERERETkjiUmJhITE0NgYCAuLi7WjiN3SYUiEREREREREbljW7ZswWg0EhQUZO0oUgxUKBIRERERERGRO3L27Fl+//13goKCcHZ2tnYcKQYqFImIiIiIiIjIHdmyZQvOzs60bdvW2lGkmKhQJCIiIiIiIiK37fTp0xw7doz27dtTrlw5a8eRYnJLhaJjx47RuXNnQkNDARg/fjy9evUiODiY4OBgduzYAcAPP/zAs88+S58+ffj3v/8NQG5uLqNGjWLAgAG8+OKLxMfHAxAXF0f//v3p378/U6ZMuQe3JiIiIiIiIiL3gslkYvPmzbi6utK6dWtrx5Fi5HCzE65evcp7771HmzZtLI6PHDnS4mfvrl69yieffEJYWBiOjo4899xzdOnShe3bt+Pm5kZISAh79uwhJCSEjz/+mA8++IAJEybg6+vLqFGj2LlzJ4899ljx36GIiIiIiIiIFKvjx49z5swZnnzySYxGo7XjSDG66Yoio9HIkiVL8PT0vOF50dHRNG7cGFdXV5ycnGjWrBlRUVGEh4fTpUsXAAICAoiKiiInJ4eEhAR8fX0B6NChA+Hh4cVwOyIiIiIiIiJyL5lMJrZs2UKlSpVo2bKlteNIMbvpiiIHBwccHAqfFhoaytdff42HhweTJk0iNTUVd3d3c7u7uzspKSkWx+3s7DAYDKSmpuLm5mY+18PDg5SUlCLHj42Nve2bKomys7NLzb2I7dBzZ7s0d7ZLc2e7NHdll+bedmnubJfmznZlZ2ezadMmTpw4wWOPPcbx48etHUmK2U0LRUV56qmnqFSpEg0bNuSLL75g0aJFNG3a1OIck8lU5LVFHb/euQANGza8k4glTmxsbKm5F7Edeu5sl+bOdmnubJfmruzS3NsuzZ3t0tzZriNHjnD06FG8vb156qmnsLPTb2TZosjIyOu23dGMtmnTxvyXumPHjhw7dgxPT09SU1PN5yQnJ+Pp6Ymnp6d5tVBubi4mk4kqVaqQlpZmPjcpKemmW9tERERERERExLpOnTpFYmIiXbp0UZGolLqjWR0+fLj518sOHDjAww8/jJ+fH7/++itXrlwhMzOTqKgo/P39CQwMZNOmTQBs376dVq1a4ejoSJ06dYiIiABgy5YttPv/2bvz8Kjqu///r5lMJskkM5OELBAgQlhTVhFRoaAsaqBgXZAqCl+q3r21+rttq3dr/XlZ77vXXbAt/bb12+W++7u/SrUqiktBEVyKioIoieJG2JeENSHLZJtMZub8/sCZO5MFEELOLM/HdZ1rzpzPOZP3zOfDOcmLc85MndpDbwkAAAAAAPS0YDCorVu3qm/fvhozZozZ5eA8Oe2lZ59//rkeffRRHTp0SDabTevXr9ett96qH/zgB0pLS5PD4dDSpUuVmpqq++67T7fffrssFovuvvtuOZ1OzZkzR5s2bdLNN98su92uZcuWSZIefPBBPfzwwwoGgxo3bpwmT5583t8sAAAAAAA4O2VlZaqvr9d3vvMdziaKY6cNikaPHq0nn3yy0/Krr76607KSkhKVlJRELEtKStLSpUs7rTt06FA9/fTTX6dWAAAAAABgAr/frzfffFO5ubncXyrOEQECAAAAAIBTeu+991RXV6dJkybJYrGYXQ7OI4IiAAAAAADQrYaGBm3YsEHFxcUaMGCA2eXgPCMoAgAAAAAA3Vq/fr38fr++9a1vmV0KegFBEQAAAAAA6NLhw4dVWlqqyZMnKycnx+xy0AsIigAAAAAAQCeGYWjNmjVyOByaMWOG2eWglxAUAQAAAACATr744gvt27dPV111ldLS0swuB72EoAgAAAAAAETw+/169dVX1bdvX1188cVml4NeRFAEAAAAAAAivPfee6qtrdXcuXNltRIdJBJ6GwAAAAAAhHk8Hm3YsEHf+MY3NHToULPLQS8jKAIAAAAAAGGvv/66/H6/5syZY3YpMAFBEQAAAAAAkCQdOnRIpaWlmjJlinJycswuByYgKAIAAAAAADIMQ2vWrJHD4dCMGTPMLgcmISgCAAAAAAD67LPPtH//fl199dVKTU01uxyYhKAIAAAAAIAE19bWprVr16pfv36aOHGi2eXARARFAAAAAAAkuI0bN6qurk5z586V1UpUkMjofQAAAAAAEpjH49Hbb7+tUaNGaciQIWaXA5MRFAEAAAAAkMDWr1+vQCCgOXPmmF0KogBBEQAAAAAACaqyslKlpaWaMmWK+vTpY3Y5iAIERQAAAAAAJCDDMLRmzRplZGRoxowZZpeDKEFQBAAAAABAAvr03G+j1gAAIABJREFU00914MABXXXVVUpNTTW7HEQJgiIAAAAAABJMW1ubXnvtNfXr108TJ040uxxEEYIiAAAAAAASzMaNG1VXV6d58+bJaiUawP9gNAAAAAAAkEA8Ho82bNig0aNHq6ioyOxyEGUIigAAAAAASCDr1q1TMBjUnDlzzC4FUYigCAAAAACABFFZWamysjJNnTpV2dnZZpeDKERQBAAAAABAAjAMQ6tXr1ZGRoamT59udjmIUgRFAAAAAAAkgG3btungwYMqKSlRSkqK2eUgShEUAQAAAAAQ53w+n1577TUVFBRowoQJZpeDKEZQBAAAAABAnHv33XdVX1+vuXPnymolCkD3GB0AAAAAAMSx+vp6vfPOOxozZoyKiorMLgdRjqAIAAAAAIA49tprr8kwDM2ePdvsUhADCIoAAAAAAIhTBw8e1CeffKKpU6cqOzvb7HIQAwiKAAAAAACIQ8FgUKtXr5bT6dQVV1xhdjmIEQRFAAAAAADEoU8++USVlZUqKSlRSkqK2eUgRhAUAQAAAAAQZ1pbW7Vu3ToNGDBAF154odnlIIYQFAEAAAAAEGfeeecdeTwezZs3T1Yrf/rjzDFaAAAAAACII7W1tXr33Xc1btw4XXDBBWaXgxhDUAQAAAAAQBx57bXXZLFYNHv2bLNLQQwiKAIAAAAAIE7s379fn376qS6//HJlZmaaXQ5iEEERAAAAAABxIBgMas2aNXK73Zo2bZrZ5SBGERQBAAAAABAHysrKdOjQIZWUlMhut5tdDmIUQREAAAAAADGutbVV69evV2FhocaPH292OYhhBEUAAAAAAMS4DRs2qKGhQfPmzZPFYjG7HMQwgiIAAAAAAGJYTU2NNm7cqAsvvFADBw40uxzEOIIiAAAAAABi2Nq1a2W1WlVSUmJ2KYgDBEUAAAAAAMSovXv36vPPP9f06dPldrvNLgdxgKAIAAAAAIAYFAwGtWbNGmVmZmrq1Klml4M4QVAEAAAAAEAM2rp1q44cOaI5c+YoOTnZ7HIQJwiKAAAAAACIMV6vV+vXr9egQYM0ZswYs8tBHCEoAgAAAAAgxvzjH/9Qc3Oz5s6dK4vFYnY5iCMERQAAAAAAxJDq6mq9//77uuiiizRgwACzy0GcISgCAAAAACCGvPrqq7LZbLr66qvNLgVxiKAIAAAAAIAYsXv3bm3fvl3Tp0+X0+k0uxzEIYIiAAAAAABiQDAY1OrVq5WVlaVvfvObZpeDOGUzu4BE0Xfv/5ZqsyIXFkyVBs2V/F7pw5913mjgLGnglVJrvVT6i87tF8yR+l8utVRJH/+6c3vRdVLfS6XGSunTxzq3D7tJyr1Qqt8rffGfndtHLpGyi6Wa7VL5E53bR/2z5C6Sqj6Wdj3buX3s/yNlDJCOfiDtfalz+4X3S2m50qF3pANrO7df9KCU4pYq3pAq3uzcPunfJFuqtP8V6fDGzu2THz35uOcF6diHkW1JKdIl/35yfuczUvUnke12lzTx/z05v/0JqXZ7ZHtqjjThX0/Of/6fkmdvZHt6f2ncv5yc3/Z7qelQZLurSBr9zyfny34leasj27OKpeIlJ+e3/ofk80S254yXht98cn7Lw1KgNbI9f5I05IaT85t+ok4Ye4w9ibF3vsaexNiL1bGnAbE99tjvnf3Y0yUn59nvdW6P9rGXteTkY6yOPbP3eyaOvZSUaZJieOyZtN+rOnZMowLV6v+tZbLZbKaMPZd/gFRcfPJJDI69c9rvhfo9znFGEQAAAAAAUc7n86myslLZWVkaNWqU2eUgjlkMwzDMLqI7paWluuiii8wuo0ds375dxaHUFegljLvYRd/FLvoudtF3iYu+j130Xeyi774ewzD0+OOPa9++fbr33nuVk5NjWi30XXw4Vd7CGUUAAAAAAESx0tJS7dy5U7NnzzY1JEJiICgCAAAAACBK1dXVac2aNRo8eLAuvfRSs8tBAiAoAgAAAAAgChmGoRdeeEGGYejGG2+U1cqf8Dj/GGUAAAAAAEShjz76SLt27dLs2bOVnZ1tdjlIEARFAAAAAABEmdraWr366qsaMmSILrnkErPLQQIhKAIAAAAAIIqELjmTpBtuuIFLztCrGG0AAAAAAESRLVu2aPfu3ZozZw6XnKHXERQBAAAAABAlampqtHbtWg0dOlSTJk0yuxwkIIIiAAAAAACiQDAY1KpVq2SxWDR//nxZLBazS0ICIigCAAAAACAKfPDBB9q7d6/mzp2rzMxMs8tBgiIoAgAAAADAZCdOnNBrr72m4cOHa+LEiWaXgwRGUAQAAAAAgIlCl5wlJSXphhtu4JIzmIqgCAAAAAAAE23evFn79u3T3Llz5Xa7zS4HCY6gCAAAAAAAk1RXV2vdunUaOXKkLrroIrPLAQiKAAAAAAAwQzAY1PPPPy+bzabrrruOS84QFQiKAAAAAAAwwfvvv68DBw5o3rx5XHKGqEFQBAAAAABAL6uqqtL69etVXFysCy+80OxygLAzCop27typWbNm6amnnpIkHTlyRIsWLdLChQt17733yufzSZJWr16tG264QTfeeKOef/55SVJbW5vuu+8+3Xzzzbr11ltVUVEhSSovL9dNN92km266ST/72c/Ox3sDAAAAACDqhC45S05O5pIzRJ3TBkXNzc36+c9/rssuuyy87Pe//70WLlyop59+WhdccIFWrVql5uZm/eEPf9ATTzyhJ598UitWrFBdXZ1eeeUVuVwuPfPMM7rzzju1fPlySdJ//Md/6MEHH9Szzz6rxsZGvfPOO+fvXQIAAAAAECU2btyogwcP6tvf/rZcLpfZ5QARThsU2e12/eUvf1FeXl542ZYtWzRz5kxJ0vTp07V582Zt27ZNY8aMkdPpVGpqqiZMmKCysjJt3rxZV155pSRp8uTJKisrk8/n06FDhzR27NiI1wAAAAAAIJ4dP35cb7zxhkaNGqVx48aZXQ7Qie20K9hsstkiV2tpaZHdbpck9enTR1VVVaqurlZ2dnZ4nezs7E7LrVarLBaLqqurI1LT0Gt0Zfv27V//XUUhr9cbN+8FsYNxF7vou9hF38Uu+i5x0fexi76LXYnad8FgUC+//LLa2tr0jW98Q+Xl5WaX9LUlat8lktMGRadjGMY5L+9uXUkqLi4+u8KizPbt2+PmvSB2MO5iF30Xu+i72EXfJS76PnbRd7ErUfvuzTffVENDgxYuXBizZxMlat/Fm9LS0m7bzupbzxwOh7xeryTp2LFjysvLU15enqqrq8PrHD9+PLw8dLZQW1ubDMNQbm6u6urqwuuGXgMAAAAAgHj0+eef680339T48ePDt2EBotFZBUWTJ0/W+vXrJUmvv/66pk6dqnHjxumzzz6Tx+NRU1OTysrKNHHiRE2ZMkXr1q2TJG3YsEGXXHKJkpOTVVRUpK1bt0a8BgAAAAAA8aayslIrV65UYWGh5s+fz7ecIaqd9tKzzz//XI8++qgOHTokm82m9evX69e//rUeeOABrVy5UgUFBbr22muVnJys++67T7fffrssFovuvvtuOZ1OzZkzR5s2bdLNN98su92uZcuWSZIefPBBPfzwwwoGgxo3bpwmT5583t8sAAAAAAC9qa6uTitWrFB6eroWL17c6R7AQLQ57QgdPXq0nnzyyU7LH3/88U7LSkpKVFJSErEsKSlJS5cu7bTu0KFD9fTTT3+dWgEAAAAAiBmtra1asWKFfD6f7rrrLmVkZJhdEnBaZ3XpGQAAAAAA6F4wGNSzzz6ro0ePauHCherbt6/ZJQFnhKAIAAAAAIAetnbtWm3fvl3z5s3TiBEjzC4HOGMERQAAAAAA9KAtW7bovffe0+TJk7kfL2IOQREAAAAAAD1k9+7devnllzV8+HDNnTvX7HKAr42gCAAAAACAHnD8+HE99dRTysvL08KFC2W18ic3Yg+jFgAAAACAc9TU1KQnnnhCNptNS5YsUWpqqtklAWeFoAgAAAAAgHPg9/v15JNPyuPxaPHixcrKyjK7JOCsERQBAAAAAHCWDMPQiy++qP379+vGG29UYWGh2SUB54SgCAAAAACAs7RhwwaVlZVp1qxZGjdunNnlAOeMoAgAAAAAgLPw6aef6vXXX9f48eM1c+ZMs8sBegRBEQAAAAAAX1NFRYWee+45FRYWav78+bJYLGaXBPQIgiIAAAAAAL6Guro6rVixQk6nU4sXL5bNZjO7JKDHEBQBAAAAAHCGWltb9cQTT6itrU1LlixRRkaG2SUBPYqgCAAAAACAMxAMBvXMM8/o2LFjuuWWW5Sfn292SUCP4/w4AAAAAABOw+/3a9WqVSovL9e1116r4cOHm10ScF4QFAEAAAAAcArNzc3661//qv3796ukpESXXnqp2SUB5w1BEQAAAAAA3aiurtYTTzyhuro63XzzzRo3bpzZJQHnFUERAAAAAABd2L9/v/76179Kku644w4NGjTI3IKAXkBQBAAAAABAB9u2bdPzzz+vzMxMLVmyRDk5OWaXBPQKgiIAAAAAAL5iGIY2bNig119/XYMGDdLixYvlcDjMLgvoNQRFAAAAAABICgQCeumll7R161aNHz9e8+fPl83Gn81ILIx4AAAAAEDCa2lp0d/+9jft3r1bM2bM0JVXXimLxWJ2WUCvIygCAAAAACS02tpaPf7446qurtb8+fM1ceJEs0sCTENQBAAAAABIWJWVlXriiSfk9/t12223aejQoWaXBJiKoAgAAAAAkJC++OILPfvss8rIyNA//dM/KT8/3+ySANMRFAEAAAAAEophGHr//ff16quvqn///lqyZIkyMjLMLguICgRFAAAAAICEEQwGtWbNGm3evFmjR4/WggULZLfbzS4LiBoERQAAAACAhNDa2qpnnnlG5eXlmjZtmkpKSmS1Ws0uC4gqBEUAAAAAgLh34sQJrVixQlVVVbr22mt16aWXml0SEJUIigAAAAAAcW3Xrl16+umnJUm3334732wGnAJBEQAAAAAgLrW/aXVeXp4WL16sPn36mF0WENUIigAAAAAAcaetrU0vvviiPv74Y40aNUoLFixQSkqK2WUBUY+gCAAAAAAQV+rr6/Xkk0+qsrJSs2bN0owZM7hpNXCGCIoAAAAAAHHjwIEDeuqpp+Tz+bRo0SKNGjXK7JKAmEJQBAAAAACICx9++KH+/ve/y+1264477lB+fr7ZJQExh6AIAAAAABDTAoGAXnnlFW3evFlDhw7VwoUL5XA4zC4LiEkERQAAAACAmNXY2Kinn35ae/fu1dSpUzV79mzuRwScA4IiAAAAAEBMOnz4sP7617+qsbFRCxYs0IQJE8wuCYh5BEUAAAAAgJjz6aef6vnnn5fD4dCdd96pAQMGmF0SEBcIigAAAAAAMSMYDOr111/X22+/rcLCQi1atEhOp9PssoC4QVAEAAAAAIgJzc3Neu6551ReXq6LL75Y3/72t2Wz8Wct0JP4FwUAAAAAiHp79uzRc889p4aGBl1zzTW67LLLZLFYzC4LiDsERQAAAACAqOX3+/XGG2/o3XffVZ8+ffT973+f+xEB5xFBEQAAAAAgKlVXV+uZZ57RoUOHNGnSJM2dO1d2u93ssoC4RlAEAAAAAIgqhmFo69atWr16tWw2m2699VaNHj3a7LKAhEBQBAAAAACIGs3NzXrxxRf1+eefa8iQIVqwYIHcbrfZZQEJg6AIAAAAABAVQjesbmxs1OzZszV16lRZrVazywISCkERAAAAAMBUHW9Yfdddd3HDasAkBEUAAAAAANNww2oguhAUAQAAAAB6HTesBqITQREAAAAAoFd5vV797W9/44bVQBQiKAIAAAAA9Jo9e/Zo1apVksQNq4EoRFAEAAAAADjvvF6v1q1bpw8++EApKSn63ve+p/79+5tdFoAOCIoAAAAAAOdVeXm5XnrpJXk8Hn3zm99UYWEhIREQpQiKAAAAAADnRWNjo1555RV98sknysvL01133aXCwkJt377d7NIAdIOgCAAAAADQowzD0LZt27RmzRp5vV7NmjVLV1xxhWw2/gQFoh3/SgEAAAAAPaaurk4vv/yyysvLVVhYqBtuuEH5+flmlwXgDBEUAQAAAADOWTAY1JYtW7Ru3ToFg0HNnTtXkydP5hvNgBhDUAQAAAAAOCdVVVV64YUXtH//fg0dOlTXX3+9srOzzS4LwFkgKAIAAAAAnJVAIKB33nlH//jHP5ScnKwbb7xREyZMkMViMbs0AGeJoAgAAAAA8LVVVlbqhRde0JEjRzRmzBhdc801cjqdZpcF4BwRFAEAAAAAzlhbW5veeOMNbdy4URkZGVq0aJFGjRpldlkAeghBEQAAAADgtJqbm/XRRx9p06ZNqq+v18UXX6w5c+YoLS3N7NIA9CCCIgAAAABAt44dO6b3339fH3/8sdra2lRUVKQFCxZoyJAhZpcG4DwgKAIAAAAARAgGgyovL9emTZu0e/du2Ww2jR8/XlOmTFG/fv3MLg/AeURQBAAAAACQJHm9Xm3dulWbNm1STU2N3G63rr76ak2aNEnp6elmlwegFxAUAQAAAECCq66u1qZNm1RaWqrW1lYVFhaqpKREo0aNUlJSktnlAehFBEUAAAAAkIAMw9CuXbv0/vvva8eOHUpKStLYsWM1ZcoUDRgwwOzyAJiEoAgAAAAAEojP51NZWZnef/99VVVVKSMjQ7NmzdIll1wip9NpdnkATEZQBAAAAAAJYt++fVq5cqXq6urUv39/LViwQGPHjpXNxp+GAE5ibwAAAAAAcS4QCOitt97Shg0blJWVpe9973saPHiwLBaL2aUBiDIERQAAAAAQx6qrq/Xss8+qsrJSEydO1Lx585SSkmJ2WQCiFEERAAAAAMQhwzBUWlqqNWvWyGq1auHChRo7dqzZZQGIcmcVFG3ZskX33nuvhg0bJkkaPny47rjjDv34xz9WIBBQbm6ufvWrX8lut2v16tVasWKFrFarFixYoBtvvFFtbW164IEHdPjwYSUlJWnp0qUaOHBgj74xAAAAAEhUzc3Neumll/TZZ59p8ODB+s53vqPMzEyzywIQA876jKJJkybp97//ffj5T3/6Uy1cuFCzZ8/Wb37zG61atUrXXnut/vCHP2jVqlVKTk7W/PnzdeWVV2rDhg1yuVxavny53nvvPS1fvly//e1ve+QNAQAAAEAi27t3r1auXKmGhgaVlJRo2rRpslqtZpcFIEb02N5iy5YtmjlzpiRp+vTp2rx5s7Zt26YxY8bI6XQqNTVVEyZMUFlZmTZv3qwrr7xSkjR58mSVlZX1VBkAAAAAkJD8fr/WrVunv/zlL0pOTtb3v/99XXHFFYREAL6Wsz6jaPfu3brzzjtVX1+ve+65Ry0tLbLb7ZKkPn36qKqqStXV1crOzg5vk52d3Wm51WqVxWKRz+cLb9/e9u3bz7bEqOL1euPmvSB2MO5iF30Xu+i72EXfJS76PnbRd/+jrq5Ob731lqqrqzVy5EhNnjxZDQ0NUfv50Hexi76Lf2cVFA0aNEj33HOPZs+erYqKCi1evFiBQCDcbhhGl9t93eWSVFxcfDYlRp3t27fHzXtB7GDcxS76LnbRd7GLvktc9H3sou9O/i310Ucfae3atbLZbLrjjjs0evRos8s6LfoudtF38aG0tLTbtrM6BzE/P19z5syRxWJRYWGhcnJyVF9fL6/XK0k6duyY8vLylJeXp+rq6vB2x48fDy+vqqqSJLW1tckwjC7PJgIAAAAAdK25uVlPPfWUXnzxRV1wwQX6wQ9+EBMhEYDodlZB0erVq/Xf//3fkqSqqiqdOHFC119/vdavXy9Jev311zV16lSNGzdOn332mTwej5qamlRWVqaJEydqypQpWrdunSRpw4YNuuSSS3ro7QAAAABA/Nu9e7d++9vfqry8XHPmzNFtt90mt9ttdlkA4sBZXXo2Y8YM3X///XrrrbfU1tamRx55RMXFxfrJT36ilStXqqCgQNdee62Sk5N133336fbbb5fFYtHdd98tp9OpOXPmaNOmTbr55ptlt9u1bNmynn5fAAAAABCzAoGAGhoaVF9fHzHV1dWpvr5eFRUVys3N1ZIlS1RQUGB2uQDiyFkFRRkZGfrzn//cafnjjz/eaVlJSYlKSkoiliUlJWnp0qVn86MBAAAAIKYZhqHa2lrV1dXJ4/F0CoPq6+vV2NjY6V6uycnJcrvdyszM1BVXXKEZM2ZwCw8APe6sv/UMAAAAAHDmampqVFZWprKyMtXU1ES0paamyuVyKTMzU3379lVmZqZcLpfcbnd4Sk1NlcViMal6AImCoAgAAAAAzhOv16tPP/1UZWVl2r9/vywWi4qKijRt2jTl5OSEw6CUlBSzSwUASQRFAAAAANCjgsGgdu7cqbKyMn355Zfy+/3KycnR1VdfrQsvvFCZmZlmlwgA3SIoAgAAAIAecPjwYZWVlemTTz5RY2Oj0tLSdPHFF2vChAkaMGAAl40BiAkERQAAAABwljwejz755BOVlZXp6NGjSkpK0siRIzVhwgSNGDFCNht/cgGILey1AAAAAOBr8Hq9Ki8vV1lZmXbt2iXDMDRw4EB9+9vf1rhx4+RwOMwuEQDOGkERAAAAAJyCYRg6cuSIduzYoR07dujAgQMyDCP8NfUTJkxQbm6u2WUCQI8gKAIAAACADlpaWrR7927t2LFDO3fulMfjkST169dPl19+uUaOHKnCwkJZrVaTKwWAnkVQBAAAACDhdXfWUGpqqoYNG6YRI0Zo+PDhcrlcZpcKAOcVQREAAACAhNTS0qJdu3Zp586d2rFjhxoaGiRJBQUFuuKKKzRixAjOGgKQcAiKAAAAAMS9YDCo48eP6/Dhwzp06JAqKipUUVEhwzCUlpamYcOGafjw4Zw1BCDhERQBAAAAiCt+v19Hjx7VoUOHdOjQIR0+fFhHjx6V3++XJCUnJ6tfv36cNQQAXSAoAgAAABCzWltbdfjw4fCZQocPH9axY8dkGIYkKTU1VQUFBbr00kvVv39/FRQUKDc3l2AIALpBUAQAAAAgJhiGoRMnTmjPnj3at2+ftm/frtbW1nB7RkaGCgoKVFxcHA6FsrKyZLFYTKwaAGILQREAAACAqFVTU6O9e/dqz5492rt3r+rr6yVJTqdTWVlZGjt2rAoKClRQUMC9hQCgBxAUAQAAAIgadXV14WBoz549qqurkySlp6erqKhIQ4YM0ZAhQ5STk6Py8nIVFxebXDEAxBeCIgAAAACm8Xg8EcFQTU2NJCktLU1FRUWaNm2aioqKlJ+fzyVkANALCIoAAAAAnDd+v1+NjY3yeDydpoqKClVVVUk6edPpwYMHa/LkySoqKlLfvn254TQAmICgCAAAAMDXFgwG1dTUJI/Ho/r6ejU0NHQKghoaGtTY2NhpW6vVKqfTqfz8fE2cOFFDhgxRQUEBwRAARAGCIgAAAACdeL1e1dXVqa6uTvX19eH59suCwWDENhaLRRkZGXI6nXK73Ro4cKBcLlenyeFwEAoBQJQiKAIAAAASTFtbmxoaGiICoI5hkNfrjdjGYrHI7XYrMzNTF1xwgTIzM+VyueR2u8MBkNPpJAACgBhHUAQAAADEAcMw1NraGr7kK3QpWFfzHUMgSXI4HHK73crKytLgwYOVmZkZMRECAUBiICgCAAAAophhGGpubu4y+Gn/vKGhQW1tbZ22t9lscjqd4XsCDR06VE6nM3wWUCgIstvtJrw7AEC0ISgCAAAATOD3+9Xc3HzaM4AaGxsVCAQ6bZ+SkhIOfAYOHBieD4VCofnU1FS+Vh4AcMYIigAAAIBzFAwG1dzcrObmZjU1Nampqem0862trV2+lsPhCIc9eXl5EaFP+yCIM4AAAOcDQREAAADQhUAgoMbGxvBZPY2NjfJ4POH50Jk/TU1N8nq9Mgyjy9ex2+1yOBxKT09Xenq6cnJyIp63PwMoIyNDNhu/ogMAzMNRCAAAAHHP7/fL6/V2Oe3atUt79uyJCINCZ/90JTU1NfwV8AUFBeHAp334E5p3OBxKTk7u5XcLAMDZIygCAABA1AsFPU1NTWppaQlf5tXc3KyWlha1trZ2GQKFlnd1j58Qr9crp9MZDn9yc3NVVFQUft6+zel0csYPACCucZQDAABArwgGg12GOV0FP10FQd2xWCxKTU1VSkqKUlNTlZqaKpfLpdzc3PDz9m0dp4qKCo0dO5YbPgMAIIIiAAAAnIJhGAoEAmptbVVra6t8Pl+nqbvwp6uze07FYrHI4XAoLS1NDodDLpdLffv2lcPhiFjecT4lJeWcQp5jx44REgEA8BWCIgAAgDgVulyrpaUlYvJ6vWpubo4IdLoKgULLurtJc0dWq7XT2To5OTlKTU1VWlpat2f0hEKflJQUWa3W8/ypAACAUyEoAgAAiHKGYai1tVWNjY3hmyyHpsbGxvAlWh0DoLa2tlO+rs1mU1paWviyLLvdLpfLJbvdLrvdrpSUlC7nu1qWlpam5ORkzswBACDGERQBAACYJBgMqqamRsePHw9/zXpXYVBTU1O3N2NOTk5Wenq60tLSlJaWptzc3PB86Cye9s9DyxwOBzdlBgAAnfDbAQAAwHkWCoSOHTsWno4fP66qqir5/f6IdVNSUpSenq6MjAxlZmaqf//+4a9cz8jICM+HJrvdbtK7AgAA8YigCAAAoIecaSCUmZmpvLw8DRs2THl5ecrLy5Pb7VZ6ejpn+QAAAFPxmwgAAEhYfr9fR48e1cGDB7Vr1y4dOHBAwWBQwWBQhmFEPHa1zDCM8LeC1dXVnTYQys/PV15enlJSUkx81wAAAN0jKAIAAAnBMAzV1tbq4MGDqqioUEVFhQ4fPhwOdlpbW+VwOGSxWGS1WsOP7ectFkun5aHnbrc7HAj17dtXubm5BEIAACDmEBQBAIC41NLSosrKSlVUVITDoaamJkntIXdKAAAZZElEQVQnbwDdv39/TZ48WQMHDtTAgQN1+PBhfeMb3zC5agAAAHMRFAEAgKgQOrMndDmXYRjh5+2Xh3R87vF4dPDgwXAoVFVVFW7Ly8vTyJEjNXDgQBUWFio/P19JSUkRP//IkSPn7b0BAADECoIiAADQK/x+v2pra1VTUxN+DE21tbVqaWnpkZ+Tnp6ugQMH6sILL1RhYaEGDBig1NTUHnltAACAeEdQBAAAekQwGFRDQ0NE+NN+3uPxRJwBZLPZlJmZqezsbBUWFsrlcoXv+SOp2/nunjscDg0cOFBZWVkRbQAAADhzBEUAAOCM+P1+1dXVdZpqa2tVW1ur+vp6BQKB8PoWi0Uul0vZ2dkaMmSIsrOzI6aMjAxZrVYT3xEAAAA6IigCAACSJK/Xq5qami6DoLq6OjU0NESsb7FY5HQ6lZmZqQEDBmj06NERQVBmZqZsNn7VAAAAiCX89gYAQAJpbW1VdXW1qqurdeLEiYj50DeChYQuDcvMzNSIESOUmZmprKys8KPL5SIIAgAAiDP8dgcAQJzx+XwRQVD7QKixsTFiXbfbrT59+mjUqFHq06ePsrOzw2FQeno69/oBAABIMARFAADEEJ/PJ4/HI4/Ho4aGhvB8aDpx4oQ8Hk/ENk6nU3369NHIkSOVk5MTnrKzs2W32016JwAAAIhGBEUAAEQBv98fDn5Cj/X19Z0CIa/X22nb5ORkOZ1OuVwuDR06NCIM6tOnj1JSUkx4RwAAAIhFBEUAAJxnobOA6uvrw1P75x6PR42NjRFfHS9JSUlJ4QAoLy9PQ4cOlcvlCk+httTUVC4RAwAAQI8gKAIA4Bw1NzerqqpK1dXV+vLLL/Xll19GhEEtLS2dtklLS5PL5ZLb7VZBQYHcbnf4eSgAcjgcfH08AAAAehVBEQAAZ8Dn84VvCh0KhULz7YMgr9ernJwcud1uZWdna9CgQXK73RGTy+Xi3kAAAACISgRFAAB8JRgMqra2tsswqL6+PmLd0LeFjRkzRrm5ucrJyVFubq6OHj2q0aNHm/QOAAAAgHNDUAQASDihr48/fvx4eAoFQ4FAILxeamqqcnJyVFRUFL45dG5u7ilvEF1VVdVbbwMAAADocQRFAIC4ZBiGmpqawiFQVVVVOBSqq6sLr2exWJSdna28vDyNGDFCubm54TOE0tPTuUk0AAAAEgpBEQAgLtTW1mrPnj06cOBAOBBqf++g5ORk5ebmatCgQcrNzVVeXl44ELLZOBwCAAAAEkERACBGNTc3a8+ePdq1a5f27NmjEydOSJIcDofy8/M1ZswY5eXlhQMht9vNN4gBAAAAp0FQBACICT6fT/v379fu3bu1e/duHTlyRIZhKCUlRUVFRbrssss0bNgw5eXlcbkYAAAAcJYIigAAUSkYDKqioiIcDB08eFCBQEBJSUkqLCzUlVdeqSFDhmjgwIGcKQQAAAD0EIIiAMA5CwaDamxslMfjUUNDQ/ibwwzDkGEYneaDwWDEtu3nvV6v9u3bp71796q1tVUWi0X9+vXTlClTNHToUA0aNEh2u70X3x0AAACQOAiKAACn5PV65fF4wlN9fX3EfENDgzweTzgE6gnZ2dkaN26chg4dqiFDhig9Pb3HXhsAAABA9wiKACBBhb4+3uPxqK6uTvX19RFT6Oyg1tbWTtumpaXJ6XTK5XIpPz9fLpdLLpdLbrdbTqdTSUlJslgs4XsFtZ9vf5lYV/M2m00ZGRnn860DAAAA6AZBEQDEIcMw1NzcrPr6etXV1XUKg0LL/H5/xHZWqzUc+vTr108jR46U0+mU2+0OL3e5XFz6BQAAAMQpgiIAiDF+vz/iUrCOl4SFHrsLgdxutwYMGKDMzEy53W653W5lZmbK5XIpIyODG0MDAAAACYygqJf8V1O1snZ/HrHs0sw+uiqnn1qDAT26d3unbaZl5+qK7Hx5/G367f4dndpn9cnX5KxcnfC16g8Hd3Vq/1ZugS5yZ+uwt0X/X+WeTu3X5Q/QGGem9rc06a+H9nVqv6lfoYanu7SzyaNnjxzs1L64/2ANSkvXZw11eulYZaf2OwYMUUFqmkrra/Rq1eFO7XcXDlMfe4o21VbpzRPHOrX/YNAIuWzJervmmN6tqerU/pOiYqVYk/R69RF9UHeiU/vDQ0dLktYcP6SPPbURbXarVQ8UfUOS9OLRCn3eWB/R7rTZ9MNBIyVJzxw5oF1NDRHt2cl23XPBcEnSikP7dKClKaK9X0qq/mngUEnSXyp260irN6L9grR0/a/+gyVJ/+fATtW0+SLah6U7dXO/CyRJ/3t/uRo6/ME/OsOt6/sOlCQt2/ulfO1uBixJF7qyNC+vvyTp3zuMO4mxF61jL2gElRo0tCgjRwcOHNDLdce139cin69NPp9PPp9P1qZmDf785Ge+r2iAmjLSJEkWi1V2u13ZOU5N/+oMoM3uVDXak2W322W325WcnKx+joyux563TvLWaVgTY+9cx56kmBt7Evu9adm5ypdieuzF4n5Pio6xN/6refZ7sTf2blSSpNgde2bv98wce5P8ARVLMTv2Yn2/dy5jL7+1RcVfzcfi2DuX/V6o3+MdQVEvsX22U74Ol2q05fSVru4nw9sq35aPO23T1m+ANCNfhqex6/aBg6RpuQqcqO26fUiTdOklChw93mW7f6RPumiCAhWH5fuoi/axksaMln/vQfk+7dweuDhFGj5M/p175Sv/onO7zSkNukBt23fJt2dn5/b0HKlfX7V9Vi5fxf5O7UZ2fyk7S22ffCnfkc4HB6OgSHI41Fb6uXzVRzu166t/xG0fbZOv487dYpG+2nn7PvxEvoa6iGafNUn6auft++Bj+Zojd94+m036auft+6BMPm9zZLs9Rfpq5+37oEw+X+Q9XnypDumGwV+1l8rXYefsczil607uvH2by+QLBiLbnZnSNQP/5/U73ES4LbOP9K2Tvzh0OTYYe7029lrrTigYDIanpGBQGw4cU2Njo8r37tWxZKuMcLshe1ubHjtRK6/XK1t2tgxXhlKsVqVZrUpKssoVlGZceaXcbrfe++ILHfUHZbVaZbVYJW9Q/ewpWrhwgSRp//PP6Ui7sdcmxl6vjD0pKsYe+71IZzT2+vWN7bEXJfu9mBx7I0ecnGe/16k96sfehIknP4dYHXtm7/dMHHtJ2XnSmHGxO/Zifb93DmPPJqs0/uKT7TE49s5pv0dQhJ50zwc7lJmZGbEsZWrByUdrkv7104pO26TkDZIkOZNsXbanDhwmSepjS+6yPe2rQVxgs3fdPmqCJOkCW0qX7Y4Jl0qShnXTnn7Z5ZKkUUkpKuqiPePyk8HYBItdxV21X5UsSbpUyRrfRbtz7snhOTVg06SuPp8bTv4P0sw2q77ZRbtuOfkw2yvN7NBuSUkJz1/TFNTsDu1WlzM8P7++Tf7tHdpzs8PzC0945d8T2Z40oF94/n8dbVKg8khEu23IBeH5f6qoV7CqJrK9eFh4/vt7TyjoiTx4JI93hed/UH5MRoebDdsn9QnPdzm2GHuSzn7sWa4JqKamRmOP1GjU5s/kb2tTW2jy+/Wfjc1qbGxUv4936IpDxyO29SdZtX7icKWkpGj8gSpNr2lQcnLyyclmU1KmW/67Fqmqqkqjtx+QbV9l+CbQ0smx55w5U5JUXLZD/j0HIl6fsWf+2NupZvZ7MTr29im2x57EMfdsx17o/5PZ78Xe2Dv8VVAUq2MvJBHH3rFp+ZJid+zF+n4v5GzGXvWAvPB8LI69c9rv3dJp9bhkMXry+4x7WGlpqS666CKzy+gR27dvV3Fx8elXBHoQ4+70DMNQa2urmpubw1NLS4uamprU2NjY5eTz+bp8rbS0NKWnpysjI0MZGRlyOp1dzmdkZJz2ZtD0Xeyi72IXfZe46PvYRd/FLvoudtF38eFUeQtnFAGIG36/PyLwaW5uVlNTk1paWjotbx8KBQKBLl/PYrFEBD+FhYURYU/HyWZjlwoAAAAgtvFXDYCo09VZPh3Dn6ampk7LuzvTR5JsNpvS09OVlpYmh8OhvLw8ORyOLqfQmUEOh4NvAAMAAACQUAiKAJw3fr9fXq83fOZOx8dTzQc7fLtCe6Gwx+FwyOl0Kj8/PxzsdJxC4dDpLvUCAAAAABAUATiNjmHP15lOdYaPdDLwSU1NDZ/Fk5mZqbS0tPDUXfjDWT4AAAAAcH4QFAEJyu/3y+PxyOPxqL6+vtNjfX29mpqa1NbWdsrXsdvtEeFOdnZ2eD4UAHU1n5qaSuADAAAAAFGGoAiIE4ZhyOfzyefzqbW1VT6fT5WVlWpqaooIhEJhUGNjY6fXSE5Oltvtlsvl0qBBg5SRkREOddoHPaEpNTWVGzgDAAAAQBzhLzyglxmGoUAgEBHotLW1RYQ8HZ+H1vP5fPJ6vZ0CodBkGEbEz/J6vUpNTZUkORwOuVwuuVwuDRgwQC6XKxwKhR7T0tJksVjM+FgAAAAAAFGAoAhxyTCMcNgSDAYjApSOYUroecflkhQMBuX3+8NhTug1O853fGwf9LSf7y7QORWLxaKUlBSlpKQoOTlZqampstvtcrlc4eV2u112uz3ieUpKio4eParx48fL5XIpOTn5LD9NAAAAAECiMDUo+sUvfqFt27bJYrHowQcf1NixY80sB70oFMD4/X4FAoGIx/ZT+zNvQlPojJr2y9qfdRNq/zphzLlKSkqS3W5XcnJyeAo9T09PDwc53U3JyckRgU/75zab7azP8rFarerTp08Pv1sAAAAAQLwyLSj68MMPdeDAAa1cuVJ79uzRgw8+qJUrV5pVznlVXV2to0ePyuFwdNl+ukDDMIxOU+gsmTNpCwaDnaZAINDlfHfLQs9DrxcIBCJe+1TPuwqCziXESUpKCp85EwpTHA6HMjMzw2fbpKSkhOdDN0xuH7aE5rsLYCwWS7itY/DT8TE5OZmbMgMAAAAA4oJpQdHmzZs1a9YsSdKQIUNUX1+vxsZGZWRkmFXSedHY2Kjly5erpaUlfK+YaGS1WpWUlCSr1RqeQs8tFkunto5TKJDpuH5o3mazyWazhefbLztdW+gsm1Dww82TAQAAAAA4P0z7i7u6ulqjRo0KP8/OzlZVVVWnoGj79u29XVqPmz17turr65WSktLtOqe7tCh0hkv7M106LjtVW/sQp+N8++2iRSAQUCAQUGtrq5qamswuJ2Z5vd64+DeUiOi72EXfxS76LnHR97GLvotd9F3sou/iX9ScmtHdpUjFxcW9XEnPKy4u1vbt2+PivSC2MO5iF30Xu+i72EXfJS76PnbRd7GLvotd9F18KC0t7bbNtBur5OXlqbq6Ovz8+PHjys3NNascAAAAAACAhGdaUDRlyhStX79ekvTFF18oLy8v7u5PBAAAAAAAEEtMu/RswoQJGjVqlG666SZZLBb97Gc/M6sUAAAAAAAAyOR7FN1///1m/ngAAAAAAAC0Y9qlZwAAAAAAAIguBEUAAAAAAACQRFAEAAAAAACArxAUAQAAAAAAQBJBEQAAAAAAAL5CUAQAAAAAAABJBEUAAAAAAAD4CkERAAAAAAAAJBEUAQAAAAAA4CsERQAAAAAAAJBEUAQAAAAAAICvEBQBAAAAAABAEkERAAAAAAAAvmIxDMMwu4julJaWml0CAAAAAABA3Lnooou6XB7VQREAAAAAAAB6D5eeAQAAAAAAQBJBEQAAAAAAAL5CUAQAAAAAAABJks3sAqLBL3/5S5WWlsrv9+uf//mfNWbMGP34xz9WIBBQbm6ufvWrX8lut2vt2rX6v//3/8pqteqyyy7TD3/4Q7W1temBBx7Q4cOHlZSUpKVLl2rgwIERr9/dOsFgUL/5zW+0atUqffDBB13WVl5ern//93+X1WqVy+XS8uXLlZaWphUrVmjNmjUyDEPXX3+9brnllt74qNDDzBp73Y2r9srLy/XII49IkkaMGKF/+7d/kyQdOXJEd999ty655BL95Cc/6ZXPKRrFWt8FAgE9/PDD2r9/v9ra2rRw4UJde+21vfVxRZVo7ruujguVlZWaN2+eRo8eLUnKysrS73//+975sKJMNPcdx+vzx6x+f+utt/Rf//VfSk5OVnZ2tn71q18pJSUlYluOlacXa/3H8fKkaO43jpWnFs19x7EyhhgJbvPmzcYdd9xhGIZh1NTUGJdffrnxwAMPGGvXrjUMwzCWL19u/O1vfzOam5uN6dOnGw0NDUYwGDTmz59v7Nq1y3jxxReNRx55xDAMw9i4caNx7733dvoZ3a3zpz/9yXjqqaeMSZMmdVvfLbfcYmzbts0wDMNYtmyZ8dRTTxkHDx40rrnmGqOtrc1obW01pk+fbng8np77UNArzBx7XY2rjm699dbwOj/60Y+Mt99+2zAMw1iyZInxy1/+0li2bFlPfhwxJRb77h//+Ifxwx/+0DAMw2hpaTGmTJliBAKBnvxYYkK0911Xx4WKigrjuuuu68FPITZFe99xvD4/zOz3xYsXh/vrgQceMFavXt1pW46VpxaL/cfxMvr7jWNl96K97zhWxo6Ev/Ts4osv1u9+9ztJksvlUktLi7Zs2aKZM2dKkqZPn67NmzcrLS1Nq1evVkZGhiwWizIzM1VXV6fNmzfryiuvlCRNnjxZZWVlnX5Gd+vceuutp01L//znP2vs2LGSpOzsbNXV1al///56+umnZbPZZLfblZqaqsbGxp75QNBrzBx7XY2r9nw+nw4dOhReJ1SLJD322GMaMmRIT38cMSUW+y4rK0sej0fBYFDNzc1KT0+X1Zp4h4Bo7jvpzI4LiSra+47j9flhZr+vWLFCTqdTfr9fVVVVys/Pj9iOY+XpxWL/cbyM7n6TOFaeSrT3HcfK2JFYe70uJCUlyeFwSJJWrVqladOmqaWlRXa7XZLUp08fVVVVSZIyMjIkSTt27NChQ4c0btw4VVdXKzs7W5JktVplsVjk8/kifkZ364Re71RC6zQ3N+vvf/+7SkpKZLValZ6eLkl67733lJWVpX79+p3rR4FeFg1jr/24aq+2tlYulyv8vKtaElks9t348eNVUFCgmTNn6uqrr9b999/f0x9LTIjmvmv/Mzuqrq7Wv/zLv+imm27S6tWrz/VjiEmx0nccr3uWmf0uSS+++KJmzZqlwsJCTZo0KWI7jpWnF4v9x/Eyuvut/c/siGNl7PQdx8rol/BBUcibb76pVatW6eGHH45YbhhGxPP9+/fr/vvv1/Lly5WcnNzpdTqu35UzWae95uZm3XXXXbrtttsi/nfqk08+0aOPPqpf//rXX+v1EF3MGnvdjavTbYf/EUt9t3XrVh05ckRvvPGGXnnlFf3617/udOBPJLHQdyGZmZm69957tXz5cv3xj3/U7373Ox0/fvyMto1H0dx3HK/PH7P6/frrr9ebb76p+vp6rVmz5oy3Q6RY6j+Ol/8jFvothGNlpGjuO46VsYGgSNLGjRv15z//WX/5y1/kdDrlcDjk9XolSceOHVNeXp4k6ejRo7r77ru1bNkyFRcXS5Ly8vLCqWxbW5sMw1AwGNSiRYu0aNEivf32212uE0p1O3rjjTfC2wYCAfn9fn3/+9/X3Llzdf3114fXKy8v10MPPaQ//elPJK4xzKyx19W4aj/2srKyIi6taF8LToq1visrK9Nll10mm82m/Px8ZWZm6tixY732eUWTaO27QCDQZb0ZGRm64YYbwjeHHD16tPbu3XteP6NoFc19x/H6/DGj3w3D0LvvvitJstlsmjlzpkpLSzlWnoVY6z+OlydFa79xrDy9aO47jpUxpEfveBSDPB6PMXfuXKO6ujq87KGHHjJefvllwzAM4+c//7nx3HPPGYZhGLfddpvx4YcfRmy/evVq48EHHzQMwzDWr19v3HfffZ1+xunWOdXNrP/4xz8af/jDHyKW+f1+47rrrjP27Nlzpm8TUcjMsdfVuOrou9/9rvHRRx8ZhmEYd955p/H++++H21544YWEvkFnLPbdunXrwjcbbGhoMC6//HKjpaXla7/3WBftfRfS/riwefNm4xe/+IVhGIbR1NRkXHXVVcbRo0fP6HXiSbT3Hcfr88Osfm9razOmTZsW/re2dOlS44knnui0LcfKU4vF/uN4Gf39FsKxsrNo7zuOlbHDYhiJfZ7sypUr9dhjj2nw4MHhZcuWLdNDDz2k1tZWFRQUaOnSpaqsrNS1114bvvmWJC1ZskRXXHGFHnroIe3fv192u13Lli3rlIIGAoEu1/n5z3+unTt3qqysTBMmTNCMGTP03e9+N2Lbb37zmxowYED4VMBLLrlE48eP149+9CONGDEivN6//uu/RtSG6Gfm2OtqXN1zzz0R2+7evVsPP/ywgsGgxo0bp5/+9Kc6duyY7r//flVVVamlpUWFhYX62c9+pqFDh57HTyr6xGLfBYNBPfLII9q1a5eCwaAWL16sb33rW+fxU4pO0d53XR0XFi1apIceekj79u1TIBDQzTffrBtuuOE8fkrRKdr7juP1+WFmv7/zzjt67LHHZLfblZOTo0cffVRpaWkR23KsPLVY7D+Ol9HfbxwruxftfcexMnYkfFAEAAAAAACAk7hHEQAAAAAAAPT/t2PHBAAAAAiD1j+1jzEgBiWKAAAAADhRBAAAAEAligAAAAA4UQQAAABAJYoAAAAAOFEEAAAAQFUD8+74AxscFIQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#hide_input\n",
    "temp = covid_19.groupby('Date')['Deaths'].sum().reset_index()\n",
    "\n",
    "covid = temp['Deaths']\n",
    "sars = [774 for i in range(len(temp))]\n",
    "ebola = [11323 for i in range(len(temp))]\n",
    "mers = [858 for i in range(len(temp))]\n",
    "h1n1 = [19654 for i in range(len(temp))]\n",
    "\n",
    "plt.figure(figsize=(20, 8))\n",
    "ax = plt.plot(temp['Date'], covid, label='COVID-19 (2019-2020)', c='#555555', alpha=0.8)\n",
    "ax = plt.plot(temp['Date'], sars, label='SARS (2003-2004)', c='#E71D36', ls='--', alpha=0.8)\n",
    "ax = plt.plot(temp['Date'], ebola, label='EBOLA (2014-2016)', c='#FF9F1C', ls='--', alpha=0.8)\n",
    "ax = plt.plot(temp['Date'], mers, label='MERS', c='#2EC4B6', ls='--', alpha=0.8)\n",
    "ax = plt.plot(temp['Date'], h1n1, label='H1N1', c='#2345BA', ls='--', alpha=0.8)\n",
    "plt.title('Number of Deaths')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.027218,
     "end_time": "2020-04-17T06:07:46.542496",
     "exception": false,
     "start_time": "2020-04-17T06:07:46.515278",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Related Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.025794,
     "end_time": "2020-04-17T06:07:46.594066",
     "exception": false,
     "start_time": "2020-04-17T06:07:46.568272",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "1. [https://www.kaggle.com/imdevskp/covid-19-analysis-viz-prediction-comparisons](https://www.kaggle.com/imdevskp/covid-19-analysis-viz-prediction-comparisons)  \n",
    "2. [https://www.kaggle.com/imdevskp/western-africa-ebola-outbreak-analysis](https://www.kaggle.com/imdevskp/western-africa-ebola-outbreak-analysis)  \n",
    "3. [https://www.kaggle.com/imdevskp/sars-2003-outbreak-analysis](https://www.kaggle.com/imdevskp/sars-2003-outbreak-analysis)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "visualization-curriculum-gF8wUgMm",
   "language": "python",
   "name": "visualization-curriculum-gf8wugmm"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  },
  "papermill": {
   "duration": 40.814329,
   "end_time": "2020-04-17T06:07:46.729579",
   "environment_variables": {},
   "exception": null,
   "input_path": "2020-03-13-COVID19-Comparitive-Analysis.ipynb",
   "output_path": "2020-03-13-COVID19-Comparitive-Analysis.ipynb",
   "parameters": {},
   "start_time": "2020-04-17T06:07:05.915250",
   "version": "2.0.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}